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\begin{algorithm}[H]
\caption{{\sc Bernoulli Factory for Matching}}
\label{alg:bernoulli_matching}
\begin{algorithmic}
\State Pick uniformly at random a permutation $\pi$ over $[n]$.
\State For each $i \in [n]$ sample the $x_{i \pi(i)}$-coin. If any sample is $0$, restart.
\State Pick uniformly at random a spanning ... | \begin{algorithm}
[H]
\caption{{\sc Bernoulli Factory for Matching}}
\begin{algorithmic}
\State Pick uniformly at random a permutation $\pi$ over $[n]$.
\State For each $i \in [n]$ sample the $x_{i \pi(i)}$-coin. If any sample is $0$, restart.
\State Pick uniformly at random a spanning tree of the complete graph $K_n$.... | "https://arxiv.org/src/2011.03865" | "2011.03865.tar.gz" | "2024-02-19" | {
"title": "combinatorial bernoulli factories",
"id": "2011.03865",
"abstract": "a bernoulli factory is an algorithmic procedure for exact sampling of certain random variables having only bernoulli access to their parameters. bernoulli access to a parameter $p \\in [0,1]$ means the algorithm does not know $... | "2024-03-15T03:22:39.599567" | {
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} | [] | "algorithm" | "63ecf243-37ae-4356-9144-bccb55ec54a9" | 593 | easy | |
\begin{algorithmic}[1]
\State \Return $\arg\max_{c\in \mathbf{C}}{ P\left( Y \lvert \mathbf{X},c\right) P\left( c\right) }$
\end{algorithmic}
| \begin{algorithmic}
[1]
\State \Return $\arg\max_{c\in \mathbf{C}}{ P\left( Y \lvert \mathbf{X},c\right) P\left( c\right) }$
\end{algorithmic} | "https://arxiv.org/src/2402.10018" | "2402.10018.tar.gz" | "2024-02-15" | {
"title": "multi-stage algorithm for group testing with prior statistics",
"id": "2402.10018",
"abstract": "in this paper, we propose an efficient multi-stage algorithm for non-adaptive group testing (gt) with general correlated prior statistics. the proposed solution can be applied to any correlated stati... | "2024-03-15T04:32:28.811580" | {
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} | [] | "algorithm" | "6bf7ab91-418f-4220-90ec-9884d22c9527" | 142 | easy | |
\begin{algorithmic}[1]
\For{each client index $i = 1,2,\dots,n$ \textbf{in parallel}}
\State initialize $\mathbf{u}_i$, $\mathbf{D}^{(i)}$;
\EndFor
\For{$a=1,2,\dots,t_1$}
\State $S_a$ $\leftarrow$ randomly select $n_s$ from $n$ clients
\For{each client ... | \begin{algorithmic}
[1]
\For{each client index $i = 1,2,\dots,n$ \textbf{in parallel}}
\State initialize $\mathbf{u}_i$, $\mathbf{D}^{(i)}$;
\EndFor
\For{$a=1,2,\dots,t_1$}
\State $S_a$ $\leftarrow$ randomly select $n_s$ from $n$ clients
\For{each client index $i \in S_a$ \textbf{in parallel}}
\State download $\mathbf{... | "https://arxiv.org/src/2301.09109" | "2301.09109.tar.gz" | "2024-02-07" | {
"title": "federated recommendation with additive personalization",
"id": "2301.09109",
"abstract": "building recommendation systems via federated learning (fl) is a new emerging challenge for advancing next-generation internet service and privacy protection. existing approaches train shared item embedding... | "2024-03-15T07:15:57.429137" | {
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} | [] | "algorithm" | "6c8fa35d-b77a-4884-899b-d2a0ed5356bc" | 729 | medium | |
\begin{algorithm}
\caption{$L$-lag-test (empirical version)}
\label{alg:llt}
\begin{algorithmic}[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$, $\alpha$, $L$, $B$
\For{$k = 1, \ldots, n-L$}
\State $X'_k \gets \left(X_k \ldots, X_{k+L}\right)$
\State $Y'_k \gets \left(Y_k, \ldots, Y_{... | \begin{algorithm}
\caption{$L$-lag-test (empirical version)}
\begin{algorithmic}
[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$, $\alpha$, $L$, $B$
\For{$k = 1, \ldots, n-L$}
\State $X'_k \gets \left(X_k \ldots, X_{k+L}\right)$
\State $Y'_k \gets \left(Y_k, \ldots, Y_{k+L}\right)$
\EndFor
\State $N \gets ... | "https://arxiv.org/src/2112.14091" | "2112.14091.tar.gz" | "2024-02-05" | {
"title": "a bootstrap test for independence of time series based on the distance covariance",
"id": "2112.14091",
"abstract": "we present a test for independence of two strictly stationary time series based on a bootstrap procedure for the distance covariance. our test detects any kind of dependence bet... | "2024-03-15T04:47:15.995017" | {
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} | [] | "algorithm" | "10e2fd0a-92e2-4bff-86a1-32eeb5514c0a" | 877 | medium | |
\begin{algorithmic}[1]
\Function{\texttt{Relax}}{~}
\State
Initialize $\bar \beta^{(0)} \leftarrow {\arg\min}_{\beta}\sum_{i=1}^n(Y_i - Z_i'\beta)^2$ through the least squares;
\State
Initialize $\bar \delta^{(0)}\leftarrow \bar \beta^{(0)} + e^{(0)}$, where $e^{(0)}\sim N(\boldsymbol 0_{d},I_{d})$;
\State
Generate $\... | \begin{algorithmic}
[1]
\Function{\texttt{Relax}}{~}
\State
Initialize $\bar \beta^{(0)} \leftarrow {\arg\min}_{\beta}\sum_{i=1}^n(Y_i - Z_i'\beta)^2$ through the least squares;
\State
Initialize $\bar \delta^{(0)}\leftarrow \bar \beta^{(0)} + e^{(0)}$, where $e^{(0)}\sim N(\boldsymbol 0_{d},I_{d})$;
\State
Generate $\... | "https://arxiv.org/src/2206.06140" | "2206.06140.tar.gz" | "2024-01-13" | {
"title": "inference for change-plane regression",
"id": "2206.06140",
"abstract": "a key challenge in analyzing the behavior of change-plane estimators is that the objective function has multiple minimizers. two estimators are proposed to deal with this non-uniqueness. for each estimator, an n-rate of con... | "2024-03-15T06:15:14.775372" | {
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} | [] | "algorithm" | "24b257f0-16d2-4883-8b72-8fe92b5f312a" | 1107 | medium | |
\begin{algorithmic}[1]
\State $\boldsymbol{J}$ = $\emptyset$
\For{$i$ in range(0,$n-1$)}
\State $S_1(\alpha) = F_i^{-1}(1-\alpha/2)$,
\State $S_2(\alpha) = F_{i+1}^{-1}(\alpha/2)$,
\State Let $S_1(\alpha) = S_2(\alpha)$, solve for solution $\alpha_i'$.
\If{$\alpha_i' \ge... | \begin{algorithmic}
[1]
\State $\boldsymbol{J}$ = $\emptyset$
\For{$i$ in range(0,$n-1$)}
\State $S_1(\alpha) = F_i^{-1}(1-\alpha/2)$,
\State $S_2(\alpha) = F_{i+1}^{-1}(\alpha/2)$,
\State Let $S_1(\alpha) = S_2(\alpha)$, solve for solution $\alpha_i'$.
\If{$\alpha_i' \geq \alpha^a*$}
\State $ \boldsymbol{J} = \boldsym... | "https://arxiv.org/src/2401.12237" | "2401.12237.tar.gz" | "2024-01-19" | {
"title": "a distribution-guided mapper algorithm",
"id": "2401.12237",
"abstract": "motivation: the mapper algorithm is an essential tool to explore shape of data in topology data analysis. with a dataset as an input, the mapper algorithm outputs a graph representing the topological features of the whole ... | "2024-03-15T07:04:57.607207" | {
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} | [] | "algorithm" | "c587d0a2-287c-4c09-80a2-b3a423534d5a" | 504 | easy | |
\begin{algorithmic}[1]
\State Let $\mathcal{J}=\{I_1,I_2,\ldots,I_m\}$ be the set of subintervals formed the chore $[0,1]$.
\State Solve the following linear program:
\begin{align}\label{eq1}
\min \quad
& \sum_{i,j =1}^{n} \sum_{k=1}^m x_{j,I_k} V_{i,j}(I_k)
\end{align}
s.t.
\begin{align}
\sum_{i=1}^n x_{i,I_k}&
= 1 ... | \begin{algorithmic}
[1]
\State Let $\mathcal{J}=\{I_1,I_2,\ldots,I_m\}$ be the set of subintervals formed the chore $[0,1]$.
\State Solve the following linear program:
\begin{align*}
\min \quad
& \sum_{i,j =1}^{n} \sum_{k=1}^m x_{j,I_k} V_{i,j}(I_k)
\end{align*}
s.t.
\begin{align*}
\sum_{i=1}^n x_{i,I_k}&
= 1 && \foral... | "https://arxiv.org/src/2303.12446" | "2303.12446.tar.gz" | "2024-02-24" | {
"title": "externalities in chore division",
"id": "2303.12446",
"abstract": "the chore division problem simulates the fair division of a heterogeneous, undesirable resource among several agents. in the fair division of chores, each agent only gets the disutility from its own piece. agents may, however, al... | "2024-03-15T03:42:06.657255" | {
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} | [] | "algorithm" | "9d382333-1f31-427c-bc83-b3b6df5f7d1d" | 848 | medium | |
\begin{algorithm}[htb]
\caption{GreedyMatch (Greedy metric bipartite matching)}\label{alg1}
\begin{algorithmic}[1]
\State Input: Two multi-sets of $n$ points $R,B$ in $Q_d$.
\State Output: A matching from $R$ to $B$.
\State$\triangleright$ The set B is shared across all threads
\Procedure{WeightedMatch}{$R,B$}
\For {$r... | \begin{algorithm}
[htb]
\caption{GreedyMatch (Greedy metric bipartite matching)}\begin{algorithmic}
[1]
\State Input: Two multi-sets of $n$ points $R,B$ in $Q_d$.
\State Output: A matching from $R$ to $B$.
\State$\triangleright$ The set B is shared across all threads
\Procedure{WeightedMatch}{$R,B$}
\For {$r \in R$}\Co... | "https://arxiv.org/src/2401.11562" | "2401.11562.tar.gz" | "2024-01-21" | {
"title": "enhancing selectivity using wasserstein distance based reweighing",
"id": "2401.11562",
"abstract": "given two labeled data-sets $\\mathcal{s}$ and $\\mathcal{t}$, we design a simple and efficient greedy algorithm to reweigh the loss function such that the limiting distribution of the neural net... | "2024-03-15T07:03:20.799704" | {
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} | [] | "algorithm" | "baca73da-9f2d-48a1-8b4a-cac54c03ef32" | 777 | medium | |
\begin{algorithmic}[1]
\For {$k=0,1, 2, \ldots$ }
\State $\bar z^k = (1 - \rho) z^k + \rho u^k$
\State $z^{k+1/2} = \bar z^k - \eta (B(u^k) + \nabla \Psi (u^k))$,
\Statex Generate $\xi^k = \begin{cases}
1,& \text{with probability} ~~ 1 - p \\
0 ,& \text{with probability} ~~ p
\end{cases},$
\label{alg_sum_vi:ste... | \begin{algorithmic}
[1]
\For {$k=0,1, 2, \ldots$ }
\State $\bar z^k = (1 - \rho) z^k + \rho u^k$
\State $z^{k+1/2} = \bar z^k - \eta (B(u^k) + \nabla \Psi (u^k))$,
\Statex Generate $\xi^k = \begin{cases}
1,& \text{with probability} ~~ 1 - p \\
0 ,& \text{with probability} ~~ p
\end{cases},$
\Statex \ \ If $\xi^k = 0$: ... | "https://arxiv.org/src/2106.07289" | "2106.07289.tar.gz" | "2024-01-24" | {
"title": "decentralized personalized federated learning for min-max problems",
"id": "2106.07289",
"abstract": "personalized federated learning (pfl) has witnessed remarkable advancements, enabling the development of innovative machine learning applications that preserve the privacy of training data. howe... | "2024-03-15T09:00:25.016199" | {
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} | [] | "algorithm" | "a9da996f-33bd-423b-a12b-67b1ad1b5d86" | 914 | medium | |
\begin{algorithmic}[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$
\State $N \gets \lfloor n/d \rfloor$
\For{$k = 1, \ldots, N$}
\State $B_{X,k} \gets (X_{(k-1)d + 1}, \ldots, X_{kd})$
\State $B_{Y,k} \gets (Y_{(k-1)d + 1}, \ldots, Y_{kd})$
\EndFor
\For{$k = 1, \ldots, N$}
... | \begin{algorithmic}
[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$
\State $N \gets \lfloor n/d \rfloor$
\For{$k = 1, \ldots, N$}
\State $B_{X,k} \gets (X_{(k-1)d + 1}, \ldots, X_{kd})$
\State $B_{Y,k} \gets (Y_{(k-1)d + 1}, \ldots, Y_{kd})$
\EndFor
\For{$k = 1, \ldots, N$}
\State $B_{X,k}^* \gets$ random ... | "https://arxiv.org/src/2112.14091" | "2112.14091.tar.gz" | "2024-02-05" | {
"title": "a bootstrap test for independence of time series based on the distance covariance",
"id": "2112.14091",
"abstract": "we present a test for independence of two strictly stationary time series based on a bootstrap procedure for the distance covariance. our test detects any kind of dependence bet... | "2024-03-15T04:47:15.995017" | {
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} | [] | "algorithm" | "d5e20090-f057-4f3e-a8b7-1a200c18eeec" | 786 | medium | |
\begin{algorithm}[H]
\caption{Fast and General MC for OU processes}\label{alg:simul1}
\begin{algorithmic}
\Require $X_{r}$ value of the process at time $r$, $\Delta t$ simulation horizon.
\State 1. Compute the characteristic function $\phi(\cdot)$ of the integral process.
\State 2. Retrieve the CDF $P(x)$ on the $x$-gr... | \begin{algorithm}
[H]
\caption{Fast and General MC for OU processes}\begin{algorithmic}
\Require $X_{r}$ value of the process at time $r$, $\Delta t$ simulation horizon.
\State 1. Compute the characteristic function $\phi(\cdot)$ of the integral process.
\State 2. Retrieve the CDF $P(x)$ on the $x$-grid by FFT inversio... | "https://arxiv.org/src/2401.15483" | "2401.15483.tar.gz" | "2024-01-27" | {
"title": "fast and general simulation of l\\'evy-driven ou processes for energy derivatives",
"id": "2401.15483",
"abstract": "l\\'evy-driven ornstein-uhlenbeck (ou) processes represent an intriguing class of stochastic processes that have garnered interest in the energy sector for their ability to capt... | "2024-03-15T05:20:32.272792" | {
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} | [] | "algorithm" | "5c81863e-90dc-41ce-95fa-829da8dcf008" | 540 | easy | |
\begin{algorithm}[H]
\caption{KSC MCMC Algorithm}\label{alg:ksc}
\begin{algorithmic}
\Require $s_0 = 4$, $\mu_0 = 0$, $\phi_0 = 0.95$, $\sigma^{2}_{\eta,0} = 0.02$
\For{\texttt{b in} $1:B_{draws}$}
\State \text{Sample states (Kalman Filter and ... | \begin{algorithm}
[H]
\caption{KSC MCMC Algorithm} \begin{algorithmic}
\Require $s_0 = 4$, $\mu_0 = 0$, $\phi_0 = 0.95$, $\sigma^{2}_{\eta,0} = 0.02$
\For{\texttt{b in} $1:B_{draws}$}
\State \text{Sample states (Kalman Filter and Smoother): } $\boldsymbol{h}_b \sim h|y^{\ast}, s_{b-1}, \phi_{b-1}, \sigma^{2}_{\eta,b-1}... | "https://arxiv.org/src/2402.12384" | "2402.12384.tar.gz" | "2024-01-27" | {
"title": "comparing mcmc algorithms in stochastic volatility models using simulation based calibration",
"id": "2402.12384",
"abstract": "simulation based calibration (sbc) is applied to analyse two commonly used, competing markov chain monte carlo algorithms for estimating the posterior distribution of... | "2024-03-15T03:46:35.266317" | {
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} | [] | "algorithm" | "c155d9ac-c686-4346-9b1a-ee4b907a3fde" | 897 | medium | |
\begin{algorithm}
\label{fig: har_st_sh}
A hybrid slice sampling transition of hit-and-run, stepping-out and shrinkage procedure
from $x$ to $y$, i.e. input $x$ and output $y$.
The stepping-out procedure on $L_t(x,\theta)$ (line of hit-and-run on level set) has inputs $x$,
$w>0$ (step size parameter from $\mathca... | \begin{algorithm}
A hybrid slice sampling transition of hit-and-run, stepping-out and shrinkage procedure
from $x$ to $y$, i.e. input $x$ and output $y$.
The stepping-out procedure on $L_t(x,\theta)$ (line of hit-and-run on level set) has inputs $x$,
$w>0$ (step size parameter from $\mathcal{R}_{d,w}$) and outputs an i... | "https://arxiv.org/src/1409.2709" | "1409.2709.tar.gz" | "2024-02-09" | {
"title": "convergence of hybrid slice sampling via spectral gap",
"id": "1409.2709",
"abstract": "it is known that the simple slice sampler has robust convergence properties, however the class of problems where it can be implemented is limited. in contrast, we consider hybrid slice samplers which are easi... | "2024-03-15T06:20:58.276849" | {
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} | [] | "algorithm" | "5a9e83a1-357e-412a-898b-4adb7ba6e1f2" | 1334 | hard | |
\begin{algorithm}[H]
\caption{APO Spread Trading Strategy Algorithm}\label{algo2}
\begin{algorithmic}[1]
\State \textbf{Input:} Stock data for two assets $S_1$ and $S_2$, buy threshold, sell threshold
\State \textbf{Output:} Trade signals for pairs trading
\State
\Procedure{Compute Hedge Ratio}{data1, data2}
\State mod... | \begin{algorithm}
[H]
\caption{APO Spread Trading Strategy Algorithm}\begin{algorithmic}
[1]
\State \textbf{Input:} Stock data for two assets $S_1$ and $S_2$, buy threshold, sell threshold
\State \textbf{Output:} Trade signals for pairs trading
\State
\Procedure{Compute Hedge Ratio}{data1, data2}
\State model $\gets$ p... | "https://arxiv.org/src/2401.14761" | "2401.14761.tar.gz" | "2024-01-26" | {
"title": "esg driven pairs algorithm for sustainable trading: analysis from the indian market",
"id": "2401.14761",
"abstract": "this paper proposes an algorithmic trading framework integrating environmental, social, and governance (esg) ratings with a pairs trading strategy. it addresses the demand for... | "2024-03-15T05:30:05.430403" | {
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} | [] | "algorithm" | "0eaefaf6-fac6-4a93-ac15-cfe451a80c42" | 1190 | hard | |
\begin{algorithm}[H]
\caption{Min-Max Projected Gradient Descent}\label{algo:minmax}
\begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\While{\(k<k_{max}\)}
\State \(\hat{J}=-\infty\)
\For{\(\sigma_{\text{test}} \in [0,1]\)}
\If{\(J(\mathbf{w},... | \begin{algorithm}
[H]
\caption{Min-Max Projected Gradient Descent} \begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\While{\(k<k_{max}\)}
\State \(\hat{J}=-\infty\)
\For{\(\sigma_{\text{test}} \in [0,1]\)}
\If{\(J(\mathbf{w}, \sigma_{\text{test}}) > \hat{J}\)}
\State \(\sigma \gets \sigma_{... | "https://arxiv.org/src/2207.06392" | "2207.06392.tar.gz" | "2024-01-25" | {
"title": "relationship design for socially-aware behavior in static games",
"id": "2207.06392",
"abstract": "autonomous agents can adopt socially-aware behaviors to reduce social costs, mimicking the way animals interact in nature and humans in society. we present a new approach to model socially-aware de... | "2024-03-15T08:38:27.674079" | {
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} | [] | "algorithm" | "d36e874d-03df-4d5a-9231-43b5899e94c2" | 858 | medium | |
\begin{algorithmic}
\Require First stage iteration number $n,K \geq 0$, Second stage iteration number $T \geq 0$, Starting point $x_1 \in \mathcal{X}$, algorithm $\mathcal{A}$
\State Consider initial start point: $x_{1}^{0}= x_1$
\For{$ 1 \leq k \leq n$}
\State Run Algorithm $\mathcal{A}$ with $K$ iterations, obtain $(... | \begin{algorithmic}
\Require First stage iteration number $n,K \geq 0$, Second stage iteration number $T \geq 0$, Starting point $x_1 \in \mathcal{X}$, algorithm $\mathcal{A}$
\State Consider initial start point: $x_{1}^{0}= x_1$
\For{$ 1 \leq k \leq n$}
\State Run Algorithm $\mathcal{A}$ with $K$ iterations, obtain $(... | "https://arxiv.org/src/2211.01758" | "2211.01758.tar.gz" | "2024-01-23" | {
"title": "optimal algorithms for stochastic complementary composite minimization",
"id": "2211.01758",
"abstract": "inspired by regularization techniques in statistics and machine learning, we study complementary composite minimization in the stochastic setting. this problem corresponds to the minimizatio... | "2024-03-15T05:49:42.262116" | {
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"num_done": {
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} | [] | "algorithm" | "3caacf00-1183-4dd6-a7a4-0b46022efd04" | 613 | easy | |
\begin{algorithmic}[1]\label{alg}
\For{$i=1...\ln(n)$}
\State Sample $u,v $ from $V$ uniformly.
\State Compute $Est$ on $\{u,v\} \times V$.
\State Compute $Int(u,v)$.
\If{ $|Int(u,v)|\geq \frac{n}{2}$}
\State Compute $Int'(u,v)$.
... | \begin{algorithmic}[1]
\For{$i=1...\ln(n)$}
\State Sample $u,v $ from $V$ uniformly.
\State Compute $Est$ on $\{u,v\} \times V$.
\State Compute $Int(u,v)$.
\If{ $|Int(u,v)|\geq \frac{n}{2}$}
\State Compute $Int'(u,v)$.
\For{$w \in Int'(u,v)$}
\State $Emb(w)=Est(u,w)$
\EndFor
\State $x_u,x_v \gets $ middle vertices of $... | "https://arxiv.org/src/2208.13855" | "2208.13855.tar.gz" | "2024-01-27" | {
"title": "determining a points configuration on the line from a subset of the pairwise distances",
"id": "2208.13855",
"abstract": "we investigate rigidity-type problems on the real line and the circle in the non-generic setting. specifically, we consider the problem of uniquely determining the position... | "2024-03-15T05:18:52.114805" | {
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"profanity... | {
"num_done": {
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} | [] | "algorithm" | "80d5754c-08fa-427e-bebe-7c8baa170c0e" | 594 | easy | |
\begin{algorithm}[H]
\centering
\caption{Top-two Thompson sampling (TTTS) with cost-aware selection rule}\label{alg:ttts}
\begin{algorithmic}[1]
\State {\bf Input:} History $\mathcal{H}_t$
\State Sample $I_t^{(1)} \sim \mathrm{TS}(\mathcal{H}_t)$ using Algorithm \ref{alg:ts}
\Repeat
\State ... | \begin{algorithm}[H]
\centering
\caption{Top-two Thompson sampling (TTTS) with cost-aware selection rule} \begin{algorithmic}
[1]
\State {\bf Input:} History $\mathcal{H}_t$
\State Sample $I_t^{(1)} \sim \mathrm{TS}(\mathcal{H}_t)$ using Algorithm \ref{alg:ts}
\Repeat
\State Sample $I_t^{(2)} \sim \mathrm{TS}(\mathca... | "https://arxiv.org/src/2402.10592" | "2402.10592.tar.gz" | "2024-02-16" | {
"title": "optimizing adaptive experiments: a unified approach to regret minimization and best-arm identification",
"id": "2402.10592",
"abstract": "practitioners conducting adaptive experiments often encounter two competing priorities: reducing the cost of experimentation by effectively assigning treatm... | "2024-03-15T03:58:07.605156" | {
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"pr... | {
"num_done": {
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} | [] | "algorithm" | "6413df63-72fc-4899-ac12-113004a2253b" | 557 | easy | |
\begin{algorithmic}[1]
\Require A batch of data $\mathcal{D}$; budget $b_t$; hyper-parameters $\eta, \lambda, \beta_1, \beta_2$; final timesteps $T_\text{final}$; timesteps $T$ and $\Delta T$ for low rank approximation
\Ensure $\Delta W_k$
\For{$t = 1,...,T_{\text{final}}$}
\State Compute the binary cross-entropy los... | \begin{algorithmic}
[1]
\Require A batch of data $\mathcal{D}$; budget $b_t$; hyper-parameters $\eta, \lambda, \beta_1, \beta_2$; final timesteps $T_\text{final}$; timesteps $T$ and $\Delta T$ for low rank approximation
\Ensure $\Delta W_k$
\For{$t = 1,...,T_{\text{final}}$}
\State Compute the binary cross-entropy loss... | "https://arxiv.org/src/2402.08075" | "2402.08075.tar.gz" | "2024-02-12" | {
"title": "efficient and scalable fine-tune of language models for genome understanding",
"id": "2402.08075",
"abstract": "although dna foundation models have advanced the understanding of genomes, they still face significant challenges in the limited scale and diversity of genomic data. this limitation ... | "2024-03-15T04:39:36.010097" | {
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"pro... | {
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} | [] | "algorithm" | "bd9ea06c-0162-4902-a4a3-c71195e18bab" | 1031 | medium | |
\begin{algorithmic}[1]
\While{$\mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{b,p}) > 0.3 \times S_{p}$, for all $p$}
\State Sample $\theta_{n,p} \sim \mbox{Unif}(a_{1,p}, a_{2,p}), n = 1, \ldots, N$
\State Simulate $\mathbf{x}_n \sim p(;\boldsymbol{\theta}_n), n = 1, \ldots, N$
\State Set $\m... | \begin{algorithmic}
[1]
\While{$\mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{b,p}) > 0.3 \times S_{p}$, for all $p$}
\State Sample $\theta_{n,p} \sim \mbox{Unif}(a_{1,p}, a_{2,p}), n = 1, \ldots, N$
\State Simulate $\mathbf{x}_n \sim p(;\boldsymbol{\theta}_n), n = 1, \ldots, N$
\State Set $\mathcal{D}_{\mbox{\script... | "https://arxiv.org/src/2303.15041" | "2303.15041.tar.gz" | "2024-02-19" | {
"title": "towards black-box parameter estimation",
"id": "2303.15041",
"abstract": "deep learning algorithms have recently shown to be a successful tool in estimating parameters of statistical models for which simulation is easy, but likelihood computation is challenging. but the success of these approach... | "2024-03-15T05:01:49.289931" | {
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... | {
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} | [] | "algorithm" | "d6cf30dd-83ae-48eb-85fc-7d754139c906" | 1358 | hard | |
\begin{algorithm} [h] \caption{Dynamic covariate balancing (DCB): two periods}\label{alg:alg1}
\begin{algorithmic}[1]
\Require Observations $(D_1, X_1, Y_{1},D_2, X_2, Y_2)$, treatment history $(d_{1}, d_2)$, finite parameters $K$, constraints $\delta_1(n,p), \delta_2(n,p)$.
\State Estimate $\beta_{d_... | \begin{algorithm}
[h] \caption{Dynamic covariate balancing (DCB): two periods} \begin{algorithmic}
[1]
\Require Observations $(D_1, X_1, Y_{1},D_2, X_2, Y_2)$, treatment history $(d_{1}, d_2)$, finite parameters $K$, constraints $\delta_1(n,p), \delta_2(n,p)$.
\State Estimate $\beta_{d_{1:2}}^1,\beta_{d_{1:2}}^2$ as in... | "https://arxiv.org/src/2103.01280" | "2103.01280.tar.gz" | "2024-01-26" | {
"title": "dynamic covariate balancing: estimating treatment effects over time with potential local projections",
"id": "2103.01280",
"abstract": "this paper studies the estimation and inference of treatment histories in panel data settings when treatments change dynamically over time. we propose a met... | "2024-03-15T05:21:52.236887" | {
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} | [] | "algorithm" | "620fbb6e-ea98-4de2-9c21-ea3aa40af803" | 1258 | hard | |
\begin{algorithmic}
\State initialization: $x_i^0 \in \mathbb{R}^n$ and $z_{i}^0 = 0$
\For{$t=0, 1, \dots$}
\vspace{-3ex}
\State
\begin{subequations}\label{eq:GTA}
\begin{align}\label{eq:GTAw}
\hspace{-0.3mm} w_i^{t+1} &= w_i^t -\gamma\sum_{j\in \mathcal{N}_i}\!\! {\ell}_{ij} (w_j^{t} - ... | \begin{algorithmic}
\State initialization: $x_i^0 \in \mathbb{R}^n$ and $z_{i}^0 = 0$
\For{$t=0, 1, \dots$}
\vspace{-3ex}
\State
\begin{subequations}
\begin{align*}
\hspace{-0.3mm} w_i^{t+1} &= w_i^t -\gamma\sum_{j\in \mathcal{N}_i}\!\! {\ell}_{ij} (w_j^{t} - \delta d_j^t) - \gamma s_i^t + \delta(d_i^{t+1} - d_i^t)
\\[... | "https://arxiv.org/src/2110.04234" | "2110.04234.tar.gz" | "2024-02-06" | {
"title": "extremum seeking tracking for derivative-free distributed optimization",
"id": "2110.04234",
"abstract": "in this paper, we deal with a network of agents that want to cooperatively minimize the sum of local cost functions depending on a common decision variable. we consider the challenging scena... | "2024-03-15T04:37:47.010213" | {
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... | {
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} | [] | "algorithm" | "087b327c-6174-4efe-90e7-c18f00613878" | 524 | easy | |
\begin{algorithm}[!ht]\caption{Dynamic KDE, query part}\label{alg:dynamic_KDE_query}
\begin{algorithmic}[1]
\State {\bf data structure} \textsc{DynamicKDE} \Comment{Theorem~\ref{thm:main_result}}
\State
\Procedure{\textsc{Query}}{$q\in \mathbb{R}^d, \epsilon \in (0,1),f_{\mathsf{KDE}} \in [0,1]$}
\For{$a=1,2,\... | \begin{algorithm}[!ht]
\caption{Dynamic KDE, query part}\begin{algorithmic}
[1]
\State {\bf data structure} \textsc{DynamicKDE} \Comment{Theorem~\ref{thm:main_result}}
\State
\Procedure{\textsc{Query}}{$q\in \mathbb{R}^d, \epsilon \in (0,1),f_{\mathsf{KDE}} \in [0,1]$}
\For{$a=1,2,\cdots,K_1$} \For{$r=1,2,\cdots,R$} \S... | "https://arxiv.org/src/2208.03915" | "2208.03915.tar.gz" | "2024-02-13" | {
"title": "dynamic maintenance of kernel density estimation data structure: from practice to theory",
"id": "2208.03915",
"abstract": "kernel density estimation (kde) stands out as a challenging task in machine learning. the problem is defined in the following way: given a kernel function $f(x,y)$ and a ... | "2024-03-15T05:48:56.340651" | {
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"num_done": {
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} | [] | "algorithm" | "b3999e9e-309f-4ede-83a5-13ea8cf3e707" | 1139 | medium | |
\begin{algorithmic}[1]
\State Sort the elements of $C_i$ by order of decreasing radius.
\ForAll{$(x_{i_j},r_{i_j}) \in C_i$}
\If{there does not exist $(x_{i_k}, r_{i_k}) \in C_i^*$ that covers $(x_{i_j}, r_{i_j})$}
\State Add $(x_{i_j}, r_{i_j})$ to $C_i^*$.
\EndIf
\EndFor
\end{algorithmic}
| \begin{algorithmic}
[1]
\State Sort the elements of $C_i$ by order of decreasing radius.
\ForAll{$(x_{i_j},r_{i_j}) \in C_i$}
\If{there does not exist $(x_{i_k}, r_{i_k}) \in C_i^*$ that covers $(x_{i_j}, r_{i_j})$}
\State Add $(x_{i_j}, r_{i_j})$ to $C_i^*$.
\EndIf
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2301.09734" | "2301.09734.tar.gz" | "2024-02-08" | {
"title": "topological learning in multi-class data sets",
"id": "2301.09734",
"abstract": "we specialize techniques from topological data analysis to the problem of characterizing the topological complexity (as defined in the body of the paper) of a multi-class data set. as a by-product, a topological cla... | "2024-03-15T07:16:13.009881" | {
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... | {
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} | [] | "algorithm" | "58f0140f-6bd1-41fb-ad1d-119bbb8b5df0" | 292 | easy | |
\begin{algorithmic}
\If{$o \in co, co \in CO^{PC}$}
\If{$ p_o \geq p_{o^{parent}_{co}}$}
$p^{obvious}_g = p_o$\;
\Else
\For{$o' \in co$}
\If{$q_{o'} > 0$}
$p^{*}_{co} = p_{o'} + p^{*}_{co}$\
$q^{*}_{co} = q_{o'} + q^{*}_{co}$\
\EndIf
... | \begin{algorithmic}
\If{$o \in co, co \in CO^{PC}$}
\If{$ p_o \geq p_{o^{parent}_{co}}$}
$p^{obvious}_g = p_o$\;
\Else
\For{$o' \in co$}
\If{$q_{o'} > 0$}
$p^{*}_{co} = p_{o'} + p^{*}_{co}$\
$q^{*}_{co} = q_{o'} + q^{*}_{co}$\
\EndIf
\EndFor
$p^{obvious}_g = \frac{p^{*}_{co}}{q^{*}_{co}}$\
\EndIf
\EndIf
\State $p^{obvi... | "https://arxiv.org/src/2402.12848" | "2402.12848.tar.gz" | "2024-02-20" | {
"title": "atlas: a model of short-term european electricity market processes under uncertainty",
"id": "2402.12848",
"abstract": "the atlas model simulates the various stages of the electricity market chain in europe, including the formulation of offers by different market actors, the coupling of europe... | "2024-03-15T03:29:36.738175" | {
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} | [] | "algorithm" | "94524db3-ed16-43b5-8abd-5b179c258c5f" | 352 | easy | |
\begin{algorithm}[t]
\caption{Sequential Covering}
\label{algo:sc}
\begin{algorithmic}[1]
\Procedure{SequentialCovering}{$\mathcal{D}$, $n$, $len$, $\beta$}
\State $\mathcal{R} \leftarrow \emptyset$
\State $\mathcal{D}' \leftarrow \mathcal{D}$
\For{$i = 1$ to $n$}
\State $r \leftarrow \textsc{RuleInduction}(l... | \begin{algorithm}
[t]
\caption{Sequential Covering}
\begin{algorithmic}
[1]
\Procedure{SequentialCovering}{$\mathcal{D}$, $n$, $len$, $\beta$}
\State $\mathcal{R} \leftarrow \emptyset$
\State $\mathcal{D}' \leftarrow \mathcal{D}$
\For{$i = 1$ to $n$}
\State $r \leftarrow \textsc{RuleInduction}(len, \beta, \mathcal{D}')... | "https://arxiv.org/src/2311.00964" | "2311.00964.tar.gz" | "2024-01-17" | {
"title": "on finding bi-objective pareto-optimal fraud prevention rule sets for fintech applications",
"id": "2311.00964",
"abstract": "rules are widely used in fintech institutions to make fraud prevention decisions, since rules are highly interpretable thanks to their intuitive if-then structure. in p... | "2024-03-15T05:59:22.584765" | {
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} | [] | "algorithm" | "ff31089c-c750-4c5a-86b2-4b0f875ac086" | 590 | easy | |
\begin{algorithmic}
\Require $n=2$, $M\in\mathbb{N}$ sufficiently large
\Require $X_{1i}$ are iid and continuous with pdf $f$ and cdf $F$ for $i=0,1,...,k-1$
\Require $X_{2i}=1+r$, $r\geq0$ for $i=0,1,...,k-1$
\Require $G_1=\{.1,.2,...,.9\}$
\State $i\gets k$ \Comment{initialize i}
\While{$i>0$}
\State $i\gets i-1$
\Fo... | \begin{algorithmic}
\Require $n=2$, $M\in\mathbb{N}$ sufficiently large
\Require $X_{1i}$ are iid and continuous with pdf $f$ and cdf $F$ for $i=0,1,...,k-1$
\Require $X_{2i}=1+r$, $r\geq0$ for $i=0,1,...,k-1$
\Require $G_1=\{.1,.2,...,.9\}$
\State $i\gets k$ \Comment{initialize i}
\While{$i>0$}
\State $i\gets i-1$
\Fo... | "https://arxiv.org/src/2402.17164" | "2402.17164.tar.gz" | "2024-02-26" | {
"title": "withdrawal success optimization in a pooled annuity fund",
"id": "2402.17164",
"abstract": "consider a closed pooled annuity fund investing in n assets with discrete-time rebalancing. at time 0, each annuitant makes an initial contribution to the fund, committing to a predetermined schedule of w... | "2024-03-15T02:40:56.763732" | {
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} | [] | "algorithm" | "adab2d01-b315-47e7-b57d-71e692da3add" | 1096 | medium | |
\begin{algorithm}
\caption{Greedy algorithm for the menu selection problem}\label{alg:greedy}
\begin{algorithmic}
\State Initialize $O\gets \emptyset$.\footnote{This can be replaced with any other menu of public goods with no change to the analysis below.}
\While{$O$ is not $(t,u)$-stable}
\If{$O$ is not $t$-feasible}... | \begin{algorithm}
\caption{Greedy algorithm for the menu selection problem}\begin{algorithmic}
\State Initialize $O\gets \emptyset$.\footnote{This can be replaced with any other menu of public goods with no change to the analysis below.}
\While{$O$ is not $(t,u)$-stable}
\If{$O$ is not $t$-feasible}
\State By definitio... | "https://arxiv.org/src/2402.11370" | "2402.11370.tar.gz" | "2024-02-17" | {
"title": "stable menus of public goods: a matching problem",
"id": "2402.11370",
"abstract": "we study a matching problem between agents and public goods, in settings without monetary transfers. since goods are public, they have no capacity constraints. there is no exogenously defined budget of goods to b... | "2024-03-15T03:42:28.124433" | {
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} | [] | "algorithm" | "f228aa68-67de-4a62-83c8-a48cf1b77d0a" | 1175 | hard | |
\begin{algorithm}
\caption{Definitive Node}
\begin{algorithmic}
\While{running}
\State object position $\gets$ detection algorithm
\If{object is detected}
\If{no predictive action in progress}
\State carry out \textit{definitive action}
\ElsIf{previous goal not within tolerance}
... | \begin{algorithm}
\caption{Definitive Node}
\begin{algorithmic}
\While{running}
\State object position $\gets$ detection algorithm
\If{object is detected}
\If{no predictive action in progress}
\State carry out \textit{definitive action}
\ElsIf{previous goal not within tolerance}
\State preempt and carry out \textit{def... | "https://arxiv.org/src/2203.00156" | "2203.00156.tar.gz" | "2024-02-19" | {
"title": "preemptive motion planning for human-to-robot indirect placement handovers",
"id": "2203.00156",
"abstract": "as technology advances, the need for safe, efficient, and collaborative human-robot-teams has become increasingly important. one of the most fundamental collaborative tasks in any sett... | "2024-03-15T04:29:43.247605" | {
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} | [] | "algorithm" | "5eea9701-8fc0-4e67-9adb-bda16d1c0676" | 393 | easy | |
\begin{algorithm}[H]
\scriptsize
\caption{The Newton's Optimization Method Modified with Adding a Multiple of the Identity}\label{Newton method}
\begin{algorithmic}
\Function{NewtonOptimization}{$\bold{M}$, $\boldsymbol{\beta}$, $\boldsymbol{\phi}$}\Comment{$\bold{M}$ represents moments; $\boldsymbol{\beta}$ is the ini... | \begin{algorithm}
[H]
\scriptsize
\caption{The Newton's Optimization Method Modified with Adding a Multiple of the Identity}\begin{algorithmic}
\Function{NewtonOptimization}{$\bold{M}$, $\boldsymbol{\beta}$, $\boldsymbol{\phi}$}\Comment{$\bold{M}$ represents moments; $\boldsymbol{\beta}$ is the initial value of paramet... | "https://arxiv.org/src/2303.02898" | "2303.02898.tar.gz" | "2024-02-19" | {
"title": "stabilizing the maximal entropy moment method for rarefied gas dynamics at single-precision",
"id": "2303.02898",
"abstract": "the maximal entropy moment method (mem) is systematic solution of the challenging problem: generating extended hydrodynamic equations valid for both dense and rarefied... | "2024-03-15T03:56:31.323537" | {
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} | [] | "algorithm" | "0d877c03-1645-43f8-8c1f-0e3c03858739" | 3349 | hard | |
\begin{algorithm}
\caption{TE2Rules}\label{alg:te2rules}
\begin{algorithmic}
\State $solutions \gets []$
\\ \Comment{Rule Generation}
\For{$k \gets 1, 2, 3, \ldots n$}
\If{$k = 1$}
\State $candidates \gets getNodeRules(model)$
\Else
\State $candidates \gets getNextStage(candidates, k)$
\EndIf
... | \begin{algorithm}
\caption{TE2Rules}\begin{algorithmic}
\State $solutions \gets []$
\\ \Comment{Rule Generation}
\For{$k \gets 1, 2, 3, \ldots n$}
\If{$k = 1$}
\State $candidates \gets getNodeRules(model)$
\Else
\State $candidates \gets getNextStage(candidates, k)$
\EndIf
\\
\For{$r \gets candidates$}
\State $p \gets g... | "https://arxiv.org/src/2206.14359" | "2206.14359.tar.gz" | "2024-01-23" | {
"title": "te2rules: explaining tree ensembles using rules",
"id": "2206.14359",
"abstract": "tree ensemble (te) models, such as gradient boosted trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. this paper ... | "2024-03-15T09:04:28.850184" | {
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} | [] | "algorithm" | "17e9cc5a-ac03-4730-8407-36fb5edfb838" | 603 | easy | |
\begin{algorithm}
\floatname{algorithm}{\bf Algorithm}
\caption{Lack-of-fit Test}
\vspace{4pt}
\hrule
\vspace{4pt}
\label{alg:LOFT}
\begin{algorithmic}[1]
\State Perform Step 1 - 3 proposed in \textbf{Algorithm} \ref{alg:EFT}. Based on $\{\check{\Lambda}_{\mathbf{C}}(t_j)\}_{ j =1}^n$, obtain the Jackknife bias-correct... | \begin{algorithm}
\floatname{algorithm}{\bf Algorithm}
\caption{Lack-of-fit Test}
\vspace{4pt}
\hrule
\vspace{4pt}
\begin{algorithmic}
[1]
\State Perform Step 1 - 3 proposed in \textbf{Algorithm} \ref{alg:EFT}. Based on $\{\check{\Lambda}_{\mathbf{C}}(t_j)\}_{ j =1}^n$, obtain the Jackknife bias-corrected estimators $T... | "https://arxiv.org/src/2310.11724" | "2310.11724.tar.gz" | "2024-02-26" | {
"title": "simultaneous nonparametric inference of m-regression under complex temporal dynamics",
"id": "2310.11724",
"abstract": "the paper considers simultaneous nonparametric inference for a wide class of m-regression models with time-varying coefficients. the covariates and errors of the regression m... | "2024-03-15T03:19:25.303660" | {
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} | [] | "algorithm" | "3975bd04-8bec-48cd-bdc5-1b9c14cc3c68" | 1196 | hard | |
\begin{algorithm}
\caption{List Viterbi Algorithm \cite{seshadri1994list}}\label{alg:cap_lva}
\hspace*{\algorithmicindent} \textbf{Input: $L, \tau, \left\{\mathbf{\pi}_i\right\}_{i=1}^N, \{\mathbf{\Phi}_i \}_{i=1}^N$} \\
\hspace*{\algorithmicindent} \textbf{Output: ${\mathbf{Z}}$}
\begin{algorithmic}[1]
\Statex \... | \begin{algorithm}
\caption{List Viterbi Algorithm \cite{seshadri1994list}}\hspace*{\algorithmicindent} \textbf{Input: $L, \tau, \left\{\mathbf{\pi}_i\right\}_{i=1}^N, \{\mathbf{\Phi}_i \}_{i=1}^N$} \\
\hspace*{\algorithmicindent} \textbf{Output: ${\mathbf{Z}}$}
\begin{algorithmic}
[1]
\Statex \textbf{\underline{Initial... | "https://arxiv.org/src/2402.10018" | "2402.10018.tar.gz" | "2024-02-15" | {
"title": "multi-stage algorithm for group testing with prior statistics",
"id": "2402.10018",
"abstract": "in this paper, we propose an efficient multi-stage algorithm for non-adaptive group testing (gt) with general correlated prior statistics. the proposed solution can be applied to any correlated stati... | "2024-03-15T04:20:09.862331" | {
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} | [] | "algorithm" | "9741bf89-a3a7-404d-a0c2-61fe903d957a" | 1864 | hard | |
\begin{algorithm}[htbp]
\caption{Noisy Nodes algorithm}\label{alg:nn}
\begin{algorithmic}[1]
\Require
\Statex$\tau$: Scale of coordinate noise
\Statex$GNN_{\theta}$: Graph Neural Network with parameter $\theta$
\Statex ${\rm NoiseHead}_{\theta_{n}}$: Network module with parameter $\theta_{n}$ for prediction of node-lev... | \begin{algorithm}
[htbp]
\caption{Noisy Nodes algorithm}\begin{algorithmic}
[1]
\Require
\Statex$\tau$: Scale of coordinate noise
\Statex$GNN_{\theta}$: Graph Neural Network with parameter $\theta$
\Statex ${\rm NoiseHead}_{\theta_{n}}$: Network module with parameter $\theta_{n}$ for prediction of node-level noise of e... | "https://arxiv.org/src/2307.10683" | "2307.10683.tar.gz" | "2024-02-26" | {
"title": "fractional denoising for 3d molecular pre-training",
"id": "2307.10683",
"abstract": "coordinate denoising is a promising 3d molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. theoretically, the objective is equivalent to learning... | "2024-03-15T02:31:31.211598" | {
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} | [] | "algorithm" | "6c26f623-77f2-4dce-88a6-0f8337a35300" | 1359 | hard | |
\begin{algorithm}[h!]
\caption{Distance matrix computation with Gzip}
\label{algo_gzip}
\scriptsize
\begin{algorithmic}[1]
\Statex \textbf{Input:}\texttt{ Set of sequences(S)}
\Statex \textbf{Output:}\texttt{ Distance Matrix(D)}
\For{\texttt{ $s_{1}$ in S\hspace{0.2cm}}}
\State \texttt{ $Es_{1} \gets encoded \hsp... | \begin{algorithm}
[h!]
\caption{Distance matrix computation with Gzip}
\scriptsize
\begin{algorithmic}
[1]
\Statex \textbf{Input:}\texttt{ Set of sequences(S)}
\Statex \textbf{Output:}\texttt{ Distance Matrix(D)}
\For{\texttt{ $s_{1}$ in S\hspace{0.2cm}}}
\State \texttt{ $Es_{1} \gets encoded \hspace{0.2cm} s_{1}$}
\St... | "https://arxiv.org/src/2402.08117" | "2402.08117.tar.gz" | "2024-02-12" | {
"title": "a universal non-parametric approach for improved molecular sequence analysis",
"id": "2402.08117",
"abstract": "in the field of biological research, it is essential to comprehend the characteristics and functions of molecular sequences. the classification of molecular sequences has seen widesp... | "2024-03-15T04:40:32.096340" | {
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} | [] | "algorithm" | "b663ba9a-2624-4817-8f27-d393d80853d6" | 1315 | hard | |
\begin{algorithm}[h]
\caption{Coupled MCMC}
\label{alg:cmcmc}
\begin{enumerate}
\item{Input: data $y_{1:T}$, level $l\in\mathbb{N}$, particle number $N\in\mathbb{N}$, iteration number $M\in\mathbb{N}$ and proposal $q_l$.}
\item{Initialize: Sample $\theta_0^{l}$ from the prior and then run Algorithm \ref{alg:dpf} with p... | \begin{algorithm}
[h]
\caption{Coupled MCMC}
\begin{enumerate}
\item{Input: data $y_{1:T}$, level $l\in\mathbb{N}$, particle number $N\in\mathbb{N}$, iteration number $M\in\mathbb{N}$ and proposal $q_l$.}
\item{Initialize: Sample $\theta_0^{l}$ from the prior and then run Algorithm \ref{alg:dpf} with parameter $\theta_... | "https://arxiv.org/src/2310.03114" | "2310.03114.tar.gz" | "2024-02-19" | {
"title": "bayesian parameter inference for partially observed stochastic volterra equations",
"id": "2310.03114",
"abstract": "in this article we consider bayesian parameter inference for a type of partially observed stochastic volterra equation (sve). sves are found in many areas such as physics and ma... | "2024-03-15T05:09:03.161347" | {
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} | [] | "algorithm" | "6f9cb0c5-bf27-416d-86c5-f27e90eca495" | 1869 | hard | |
\begin{algorithmic}[1]
\Require $n,p\in\mathbb N, \omega \in [0,1], x\in\mathbb R^n, v \in\mathbb R^{n}$.
\State Find a sorting permutation $\sigma$ of vector $x$.
\State Apply the $\sigma$ to $x$ and $v$, in place.
\For{$\tilde \omega = 1,0$}
\If{$\tilde \omega = 0$}
\Comment{Shift $v$ by one place because of 0s o... | \begin{algorithmic}
[1]
\Require $n,p\in\mathbb N, \omega \in [0,1], x\in\mathbb R^n, v \in\mathbb R^{n}$.
\State Find a sorting permutation $\sigma$ of vector $x$.
\State Apply the $\sigma$ to $x$ and $v$, in place.
\For{$\tilde \omega = 1,0$}
\If{$\tilde \omega = 0$}
\Comment{Shift $v$ by one place because of 0s on d... | "https://arxiv.org/src/2401.15205" | "2401.15205.tar.gz" | "2024-01-26" | {
"title": "csranks: an r package for estimation and inference involving ranks",
"id": "2401.15205",
"abstract": "this article introduces the r package csranks for estimation and inference involving ranks. first, we review methods for the construction of confidence sets for ranks, namely marginal and simult... | "2024-03-15T05:34:18.919334" | {
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} | [] | "algorithm" | "9d127570-44a1-4dbc-b561-5220d71eed5d" | 1162 | hard | |
\begin{algorithm}
\caption{Multiplication of indicator matrix of vector $x$ with arbitrary vector $v$}
\label{alg:ind-mat-mult}
\begin{algorithmic}[1]
\Require $n,p\in\mathbb N, \omega \in [0,1], x\in\mathbb R^n, v \in\mathbb R^{n}$.
\State Find a sorting permutation $\sigma$ of vector $x$.
\State Apply the $\sigma$ to... | \begin{algorithm}
\caption{Multiplication of indicator matrix of vector $x$ with arbitrary vector $v$}
\begin{algorithmic}
[1]
\Require $n,p\in\mathbb N, \omega \in [0,1], x\in\mathbb R^n, v \in\mathbb R^{n}$.
\State Find a sorting permutation $\sigma$ of vector $x$.
\State Apply the $\sigma$ to $x$ and $v$, in place.
... | "https://arxiv.org/src/2401.15205" | "2401.15205.tar.gz" | "2024-01-26" | {
"title": "csranks: an r package for estimation and inference involving ranks",
"id": "2401.15205",
"abstract": "this article introduces the r package csranks for estimation and inference involving ranks. first, we review methods for the construction of confidence sets for ranks, namely marginal and simult... | "2024-03-15T05:34:18.919334" | {
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} | [] | "algorithm" | "86deadbe-3f00-473d-85a1-1eebf50a7ec8" | 1281 | hard | |
\begin{algorithm}[H]
\caption{Reconstruction($\mathcal{P}$)}
\begin{algorithmic}[1]\label{alg}
\For{$i=1...\ln(n)$}
\State Sample $u,v $ from $V$ uniformly.
\State Compute $Est$ on $\{u,v\} \times V$.
\State Compute $Int(u,v)$.
\If{ $|Int(u,v)|\geq \f... | \begin{algorithm}
[H]
\caption{Reconstruction($\mathcal{P}$)}
\begin{algorithmic}[1]
\For{$i=1...\ln(n)$}
\State Sample $u,v $ from $V$ uniformly.
\State Compute $Est$ on $\{u,v\} \times V$.
\State Compute $Int(u,v)$.
\If{ $|Int(u,v)|\geq \frac{n}{2}$}
\State Compute $Int'(u,v)$.
\For{$w \in Int'(u,v)$}
\State $Emb(w)=... | "https://arxiv.org/src/2208.13855" | "2208.13855.tar.gz" | "2024-01-27" | {
"title": "determining a points configuration on the line from a subset of the pairwise distances",
"id": "2208.13855",
"abstract": "we investigate rigidity-type problems on the real line and the circle in the non-generic setting. specifically, we consider the problem of uniquely determining the position... | "2024-03-15T05:18:52.114805" | {
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} | [] | "algorithm" | "3a7a9213-9913-4cde-9483-1ca68be7bab2" | 672 | easy | |
\begin{algorithm}[h]
\caption{Randomized for Decentralized Min-Max (RDMM)}
\label{alg_sum}
\hspace*{\algorithmicindent} {\bf Parameters:} stepsize $\gamma$, probability $p$, probability $\rho$\\
\hspace*{\algorithmicindent} {\bf Initialization:} choose $ x^0,y^0$, $x^0_m = x^0$, $y^0_m = y^0$ for all $m$
\begin{... | \begin{algorithm}
[h]
\caption{Randomized for Decentralized Min-Max (RDMM)}
\hspace*{\algorithmicindent} {\bf Parameters:} stepsize $\gamma$, probability $p$, probability $\rho$\\
\hspace*{\algorithmicindent} {\bf Initialization:} choose $ x^0,y^0$, $x^0_m = x^0$, $y^0_m = y^0$ for all $m$
\begin{algorithmic}
[1]
\For ... | "https://arxiv.org/src/2106.07289" | "2106.07289.tar.gz" | "2024-01-24" | {
"title": "decentralized personalized federated learning for min-max problems",
"id": "2106.07289",
"abstract": "personalized federated learning (pfl) has witnessed remarkable advancements, enabling the development of innovative machine learning applications that preserve the privacy of training data. howe... | "2024-03-15T09:00:25.016199" | {
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} | [] | "algorithm" | "ad3c6e42-e72f-4f1e-99cd-7a2b8eb41b3e" | 2610 | hard | |
\begin{algorithmic}
\Require $\eta>0$\\
\State $x_{k+1}=x_k-\eta \nabla f(x_k)$
\end{algorithmic}
| \begin{algorithmic}
\Require $\eta>0$\\
\State $x_{k+1}=x_k-\eta \nabla f(x_k)$
\end{algorithmic} | "https://arxiv.org/src/2309.04877" | "2309.04877.tar.gz" | "2024-02-26" | {
"title": "a gentle introduction to gradient-based optimization and variational inequalities for machine learning",
"id": "2309.04877",
"abstract": "the rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. further progress hin... | "2024-03-15T03:14:09.276985" | {
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} | [] | "algorithm" | "075ca4b9-ef3b-4779-9e2b-06af9f6e4597" | 97 | easy | |
\begin{algorithm}[htb]
\caption{Recover Pathways}\label{path:alg}
\begin{algorithmic}[1]
\Statex Input: The underlying linear dynamical systems matrices $\tilde{B}$ and $\tilde{B'}$ and the correlations between each of the coordinates (corresponding to genes) and the two phenotypes of interest.
\Statex Output: A set of... | \begin{algorithm}
[htb]
\caption{Recover Pathways}\begin{algorithmic}
[1]
\Statex Input: The underlying linear dynamical systems matrices $\tilde{B}$ and $\tilde{B'}$ and the correlations between each of the coordinates (corresponding to genes) and the two phenotypes of interest.
\Statex Output: A set of pathways of a ... | "https://arxiv.org/src/2401.11858" | "2401.11858.tar.gz" | "2024-01-22" | {
"title": "approximating a linear dynamical system from non-sequential data",
"id": "2401.11858",
"abstract": "given non-sequential snapshots from instances of a dynamical system, we design a compressed sensing based algorithm that reconstructs the dynamical system. we formally prove that successful recons... | "2024-03-15T07:04:04.342410" | {
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} | [] | "algorithm" | "e80af0ad-8e6d-41f9-add9-9d8d2a426ab3" | 962 | medium | |
\begin{algorithm}[H]
\caption{Model-based deterministic policy REINFORCE} \label{alg: det reinforce}
\begin{algorithmic} [1]
\State Initialize deterministic policy $\mu_\theta$.
\State Choose a batch size $K$, a gradient based optimization algorithm, a corresponding learning rate $\lambda > 0$, a time step size $\Delta... | \begin{algorithm}
[H]
\caption{Model-based deterministic policy REINFORCE} \begin{algorithmic}
[1]
\State Initialize deterministic policy $\mu_\theta$.
\State Choose a batch size $K$, a gradient based optimization algorithm, a corresponding learning rate $\lambda > 0$, a time step size $\Delta t$ and a stopping criteri... | "https://arxiv.org/src/2211.02474" | "2211.02474.tar.gz" | "2024-02-15" | {
"title": "connecting stochastic optimal control and reinforcement learning",
"id": "2211.02474",
"abstract": "in this paper the connection between stochastic optimal control and reinforcement learning is investigated. our main motivation is to apply importance sampling to sampling rare events which can be... | "2024-03-15T04:03:02.445897" | {
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} | [] | "algorithm" | "4d5cb538-dc46-4f72-8994-0e813f48c45a" | 898 | medium | |
\begin{algorithm}[h!]
\caption{zCDP-NFL}
\label{alg_zCDP_NFL}
\begin{algorithmic}[1]
\item[] \textbf{Initialization:} ${\bf w}_k^{(0)}=\mathbf{0}$, $\boldsymbol{\gamma}_k^{(0)}=\mathbf{0}$, $\forall k \in \mathcal{K}$
\item[] \textit{-- Procedure at client $k$ --}
\item[] \textbf{For} iteration $n = 1, 2, \... | \begin{algorithm}
[h!]
\caption{zCDP-NFL}
\begin{algorithmic}
[1]
\item[] \textbf{Initialization:} ${\bf w}_k^{(0)}=\mathbf{0}$, $\boldsymbol{\gamma}_k^{(0)}=\mathbf{0}$, $\forall k \in \mathcal{K}$
\item[] \textit{-- Procedure at client $k$ --}
\item[] \textbf{For} iteration $n = 1, 2, \hdots$:
\begin{align*}
{\bf w}_... | "https://arxiv.org/src/2306.14012" | "2306.14012.tar.gz" | "2024-02-21" | {
"title": "private networked federated learning for nonsmooth objectives",
"id": "2306.14012",
"abstract": "this paper develops a networked federated learning algorithm to solve nonsmooth objective functions. to guarantee the confidentiality of the participants with respect to each other and potential eave... | "2024-03-15T04:36:37.987005" | {
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} | [] | "algorithm" | "f9526157-cf01-4965-9515-4ceda777786c" | 903 | medium | |
\begin{algorithm}[t]
\caption{Procedure of the component-specific aggregation for Micro-disentanglement}
\footnotesize
\label{algorithm1}
\begin{algorithmic}[1]
\Require $\left \{ \mathbf{x}_i \in \mathbb{R}^f\right \} $: the set of node feature vectors, $i \in \tilde{\mathbf{N}}\left ( u \right )$;
\Ens... | \begin{algorithm}
[t]
\caption{Procedure of the component-specific aggregation for Micro-disentanglement}
\footnotesize
\begin{algorithmic}[1]
\Require $\left \{ \mathbf{x}_i \in \mathbb{R}^f\right \} $: the set of node feature vectors, $i \in \tilde{\mathbf{N}}\left ( u \right )$;
\Ensure $\mathbf{h}_u$, $\mathbf{c}_u... | "https://arxiv.org/src/2103.07295" | "2103.07295.tar.gz" | "2024-01-24" | {
"title": "adversarial graph disentanglement",
"id": "2103.07295",
"abstract": "a real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. however, most existing methods lack consideration of the intrinsic differences in relations between n... | "2024-03-15T08:58:36.178534" | {
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} | [] | "algorithm" | "007657ac-7dc8-4e1b-bf6f-cfa73422c7d0" | 1981 | hard | |
\begin{algorithmic}[1]
\State {\bfseries }\textbf{for} i = 1,2,$\cdots$, T \textbf{do}
\State {\bfseries } \quad Automatically transcribe dialogue turn pairs $(S^p_i,S^t_i)$
\State {\bfseries }\quad \textbf{for} $(I^p_j, I^t_j) \in$ inventories $(I^p, I^t)$ \textbf{do}
\State {\bfseries }\quad \quad Score $W^{p_i}... | \begin{algorithmic}
[1]
\State {\bfseries }\textbf{for} i = 1,2,$\cdots$, T \textbf{do}
\State {\bfseries } \quad Automatically transcribe dialogue turn pairs $(S^p_i,S^t_i)$
\State {\bfseries }\quad \textbf{for} $(I^p_j, I^t_j) \in$ inventories $(I^p, I^t)$ \textbf{do}
\State {\bfseries }\quad \quad Score $W^{p_i}_{j}... | "https://arxiv.org/src/2402.14701" | "2402.14701.tar.gz" | "2024-02-22" | {
"title": "compass: computational mapping of patient-therapist alliance strategies with language modeling",
"id": "2402.14701",
"abstract": "the therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment. traditionally, working alliance assessment relies on que... | "2024-03-15T03:21:50.438155" | {
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} | [] | "algorithm" | "cf032692-19a5-4d39-92ed-352dff0bc225" | 552 | easy | |
\begin{algorithm}
\caption{MPS Encoding Procedure}
\hspace*{\algorithmicindent}
\begin{algorithmic}[1]
\Require{ A degree-$p$ piece-wise function $f_\ell(x) = \sum_{j=0}^p a_{j}^{(\ell)} x^j $. System size ${N}$. Domain [a,b]. Support bit $k$.}
\Ensure{A $\chi \le 2^k(p+1)$ MPS, $\bf{M}_T$ which encodes $f_\ell(x)$... | \begin{algorithm}
\caption{MPS Encoding Procedure}
\hspace*{\algorithmicindent}
\begin{algorithmic}
[1]
\Require{ A degree-$p$ piece-wise function $f_\ell(x) = \sum_{j=0}^p a_{j}^{(\ell)} x^j $. System size ${N}$. Domain [a,b]. Support bit $k$.}
\Ensure{A $\chi \le 2^k(p+1)$ MPS, $\bf{M}_T$ which encodes $f_\ell(x)$}
\... | "https://arxiv.org/src/2303.01562" | "2303.01562.tar.gz" | "2024-02-16" | {
"title": "quantum state preparation of normal distributions using matrix product states",
"id": "2303.01562",
"abstract": "state preparation is a necessary component of many quantum algorithms. in this work, we combine a method for efficiently representing smooth differentiable probability distributions... | "2024-03-15T04:16:05.461100" | {
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} | [] | "algorithm" | "19e2bdac-427d-457c-9927-d88ff797cdfe" | 592 | easy | |
\begin{algorithmic}
\Require $n > 0$
\State \textbf{Step 1} : Choose $u$ and $v$ such that the hypothesis of Theorem \ref{prop_dec_gen} are satisfied
\State \textbf{Step 2} : Generate a vector $U$ of $n$ i.i.d random variables of law $\mathcal{N}(0,1)$
\State \textbf{Step 3} : Set $V := \exp\bigg[-\theta\bigg(\hat{\ze... | \begin{algorithmic}
\Require $n > 0$
\State \textbf{Step 1} : Choose $u$ and $v$ such that the hypothesis of Theorem \ref{prop_dec_gen} are satisfied
\State \textbf{Step 2} : Generate a vector $U$ of $n$ i.i.d random variables of law $\mathcal{N}(0,1)$
\State \textbf{Step 3} : Set $V := \exp\bigg[-\theta\bigg(\hat{\zet... | "https://arxiv.org/src/2105.08804" | "2105.08804.tar.gz" | "2024-02-20" | {
"title": "efficient approximations for utility-based pricing",
"id": "2105.08804",
"abstract": "in a context of illiquidity, the reservation price is a well-accepted alternative to the usual martingale approach which does not apply. however, this price is not available in closed form and requires numerica... | "2024-03-15T03:14:06.406557" | {
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} | [] | "algorithm" | "3d593311-5722-4e13-87a9-aa19e91f493d" | 1043 | medium | |
\begin{algorithm}
\caption{SBC}\label{alg:sbc}
\begin{algorithmic}
\For{\texttt{k in} $1:5000$}
\State \text{Draw from joint prior: } $\boldsymbol{\theta}^{sim}_k \sim\pi (\boldsymbol{\theta})$
\State \text{Simulate data set with 1000 observations: } $\boldsym... | \begin{algorithm}
\caption{SBC} \begin{algorithmic}
\For{\texttt{k in} $1:5000$}
\State \text{Draw from joint prior: } $\boldsymbol{\theta}^{sim}_k \sim\pi (\boldsymbol{\theta})$
\State \text{Simulate data set with 1000 observations: } $\boldsymbol{y}^{sim}_k \sim \pi(\boldsymbol{y}|\boldsymbol{\theta}^{sim}_k)$
\State... | "https://arxiv.org/src/2402.12384" | "2402.12384.tar.gz" | "2024-01-27" | {
"title": "comparing mcmc algorithms in stochastic volatility models using simulation based calibration",
"id": "2402.12384",
"abstract": "simulation based calibration (sbc) is applied to analyse two commonly used, competing markov chain monte carlo algorithms for estimating the posterior distribution of... | "2024-03-15T03:27:09.183136" | {
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} | [] | "algorithm" | "90e559be-54c3-4e8c-95ae-f7dee9c1f90f" | 693 | easy | |
\begin{algorithm}
\label{algor2}Let $C=\left( c_{ij}\right) $ be an $m\times n$ BPM with
non-null maximal equalizer $E=(e_{ij})$.
\indent For each $c_{ij}=\frac{B_{1}^{ij}|B_{2}^{ij}|\cdots|B_{r}^{ij}}%
{A_{1}^{ij}|A_{2}^{ij}|\cdots|A_{r}^{ij^{\mathstrut}}}$ in $C:$
\indent\indent Let $e$ be the first element in $e... | \begin{algorithm}
Let $C=\left( c_{ij}\right) $ be an $m\times n$ BPM with
non-null maximal equalizer $E=(e_{ij})$.
\indent For each $c_{ij}=\frac{B_{1}^{ij}|B_{2}^{ij}|\cdots|B_{r}^{ij}}%
{A_{1}^{ij}|A_{2}^{ij}|\cdots|A_{r}^{ij^{\mathstrut}}}$ in $C:$
\indent\indent Let $e$ be the first element in $e_{ij}$.
\indent \i... | "https://arxiv.org/src/2111.05799" | "2111.05799.tar.gz" | "2024-01-29" | {
"title": "computing the dimension of a bipartition matrix",
"id": "2111.05799",
"abstract": "this article presents a computer program that computes the dimension of a bipartition matrix. its dimension has three independent components: row dimension, column dimension, and entry dimension. the program appli... | "2024-03-15T05:13:52.306224" | {
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} | [] | "algorithm" | "f69850cb-6a6e-4abd-89f6-55a2e01f5153" | 3869 | hard | |
\begin{algorithmic}[1]
\State For $\beta\in (1,\frac{3}{2})$ set $\varepsilon = \frac{3}{2}-\beta$ and $T = L^{2(1-\varepsilon)}$. For $i=1,\cdots,d$, solve for the approximate first-order corrector $\phi_{i,T}^{(L)}$:
\begin{equation}\label{eqn:phiTL}
\dfrac{1}{T}\phi_{i,T}^{(L)}-\nabla \cdot a \nabla \phi_{i,T}^{(L... | \begin{algorithmic}
[1]
\State For $\beta\in (1,\frac{3}{2})$ set $\varepsilon = \frac{3}{2}-\beta$ and $T = L^{2(1-\varepsilon)}$. For $i=1,\cdots,d$, solve for the approximate first-order corrector $\phi_{i,T}^{(L)}$:
\begin{equation*}
\dfrac{1}{T}\phi_{i,T}^{(L)}-\nabla \cdot a \nabla \phi_{i,T}^{(L)} =\nabla\cdot a... | "https://arxiv.org/src/2109.01616" | "2109.01616.tar.gz" | "2024-01-11" | {
"title": "optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media",
"id": "2109.01616",
"abstract": "we are interested in numerical algorithms for computing the electrical field generated by a charge distribution localized on scale $\\ell$ in a... | "2024-03-15T06:22:17.156672" | {
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} | [] | "algorithm" | "3b44e8b9-4d16-4467-81ba-03d224d36301" | 2900 | hard | |
\begin{algorithm}[htb]
\caption{ScaledGreedyReweight (scale distributions and call bipartite matching)}\label{alg2}
\begin{algorithmic}[1]
\State Input: Two probability distributions $\P_B,\P_R$ supported on $B,R\subset Q_d$, and a tilt factor $\alpha\in(0,1)$.
\State Output: Probability distribution $\P_B'$ supported ... | \begin{algorithm}
[htb]
\caption{ScaledGreedyReweight (scale distributions and call bipartite matching)}\begin{algorithmic}
[1]
\State Input: Two probability distributions $\P_B,\P_R$ supported on $B,R\subset Q_d$, and a tilt factor $\alpha\in(0,1)$.
\State Output: Probability distribution $\P_B'$ supported on $B$. $\P... | "https://arxiv.org/src/2401.11562" | "2401.11562.tar.gz" | "2024-01-21" | {
"title": "enhancing selectivity using wasserstein distance based reweighing",
"id": "2401.11562",
"abstract": "given two labeled data-sets $\\mathcal{s}$ and $\\mathcal{t}$, we design a simple and efficient greedy algorithm to reweigh the loss function such that the limiting distribution of the neural net... | "2024-03-15T07:13:16.850922" | {
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} | [] | "algorithm" | "25f8bc0a-8006-4207-a351-20765eae61d3" | 1107 | medium | |
\begin{algorithm}
\caption{Func-LiNGAM (Can be regarded as vector-based DirectLiNGAM but with FPCA preprocessing.)}
\label{algo1}
\begin{algorithmic}[1]
\State \textbf{Input:} Each function has $W$ time points, then construct $Wp$-dimensional random vector ${f}$ ($W$: Full-time points) for $p$ functions, a set of its v... | \begin{algorithm}
\caption{Func-LiNGAM (Can be regarded as vector-based DirectLiNGAM but with FPCA preprocessing.)}
\begin{algorithmic}
[1]
\State \textbf{Input:} Each function has $W$ time points, then construct $Wp$-dimensional random vector ${f}$ ($W$: Full-time points) for $p$ functions, a set of its variable subsc... | "https://arxiv.org/src/2401.09641" | "2401.09641.tar.gz" | "2024-01-17" | {
"title": "functional linear non-gaussian acyclic model for causal discovery",
"id": "2401.09641",
"abstract": "in causal discovery, non-gaussianity has been used to characterize the complete configuration of a linear non-gaussian acyclic model (lingam), encompassing both the causal ordering of variables a... | "2024-03-15T07:19:39.274028" | {
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} | [] | "algorithm" | "a6e0c1ce-e220-4765-8133-591483e0b7ea" | 1745 | hard | |
\begin{algorithm}
\caption{Extragradient Method}
\label{alg:lec3-extragradient}
\begin{algorithmic}
\Require $\eta > 0$\\
\State $x_{k+1} = x_k - \eta F(\Tilde{x}_k),$ where\\
\\
$\Tilde{x}_k = x_k - \eta F(x_k)$
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Extragradient Method}
\begin{algorithmic}
\Require $\eta > 0$\\
\State $x_{k+1} = x_k - \eta F(\Tilde{x}_k),$ where\\
\\
$\Tilde{x}_k = x_k - \eta F(x_k)$
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2309.04877" | "2309.04877.tar.gz" | "2024-02-26" | {
"title": "a gentle introduction to gradient-based optimization and variational inequalities for machine learning",
"id": "2309.04877",
"abstract": "the rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. further progress hin... | "2024-03-15T03:14:09.276985" | {
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} | [] | "algorithm" | "d809b30b-07a6-41d0-b1cb-4a292afc86f2" | 215 | easy | |
\begin{algorithmic}
\Require $\theta_0 \in \mathbb{R}^n, b_0 > 0$
\For{$k \in \mathbb{N}$}
\State $\theta_{k} = \theta_{k-1} - \frac{1}{b_{k-1}} \dot{F}(\theta_{k-1})$
\State $b_k = b_{k-1} + \frac{||\dot{F}(\theta_{k})||_2^2}{b_{k-1}}$
\EndFor
\end{algorithmic}
| \begin{algorithmic}
\Require $\theta_0 \in \mathbb{R}^n, b_0 > 0$
\For{$k \in \mathbb{N}$}
\State $\theta_{k} = \theta_{k-1} - \frac{1}{b_{k-1}} \dot{F}(\theta_{k-1})$
\State $b_k = b_{k-1} + \frac{||\dot{F}(\theta_{k})||_2^2}{b_{k-1}}$
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2309.10894" | "2309.10894.tar.gz" | "2024-02-15" | {
"title": "a novel gradient methodology with economical objective function evaluations for data science applications",
"id": "2309.10894",
"abstract": "gradient methods are experiencing a growth in methodological and theoretical developments owing to the challenges of optimization problems arising in dat... | "2024-03-15T05:23:50.845023" | {
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} | [] | "algorithm" | "b8b6e914-0741-4441-8e4d-98dd2fb4c052" | 262 | easy | |
\begin{algorithmic}[1]
\State Initialize: $A_0\gets \Phi$
\For {$i \in [m]$}
\State Let $u_i$ be the element $u\in P_i$ maximizing $f(u~|~A_{i-1}) := f(A_{i-1}\cup \{u\}) - f(A_{i-1})$.
\State $A_i\gets A_{i-1}\cup \{u_i\}$
\EndFor
\end{algorithmic}
| \begin{algorithmic}
[1]
\State Initialize: $A_0\gets \Phi$
\For {$i \in [m]$}
\State Let $u_i$ be the element $u\in P_i$ maximizing $f(u~|~A_{i-1}) := f(A_{i-1}\cup \{u\}) - f(A_{i-1})$.
\State $A_i\gets A_{i-1}\cup \{u_i\}$
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2208.03367" | "2208.03367.tar.gz" | "2024-02-12" | {
"title": "sublinear time algorithm for online weighted bipartite matching",
"id": "2208.03367",
"abstract": "online bipartite matching is a fundamental problem in online algorithms. the goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each verte... | "2024-03-15T06:18:53.303533" | {
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} | [] | "algorithm" | "61951fcc-e041-412f-90c5-01c349f7d42b" | 250 | easy | |
\begin{algorithm}
\caption{Bootstrap particle filter algorithm}\label{euclidBF1}
\begin{algorithmic}[1]
\For {$k = 1,...,\textit{N}$}
\State $t=1$, \text{draw sample} $X^{k}_{(1)} \sim p(X_{(1)})$;
\EndFor
\For {$t = 2,...,\textit{T}$}
\For {$k = 1,...,\textit{N}$}
\State Draw sample $X_{(t)}^k \sim p(X_{(t)} \vert ... | \begin{algorithm}
\caption{Bootstrap particle filter algorithm}\begin{algorithmic}
[1]
\For {$k = 1,...,\textit{N}$}
\State $t=1$, \text{draw sample} $X^{k}_{(1)} \sim p(X_{(1)})$;
\EndFor
\For {$t = 2,...,\textit{T}$}
\For {$k = 1,...,\textit{N}$}
\State Draw sample $X_{(t)}^k \sim p(X_{(t)} \vert X^{*k}_{(t-1)})$;
\S... | "https://arxiv.org/src/2105.04789" | "2105.04789.tar.gz" | "2024-02-10" | {
"title": "innovative approaches in soil carbon sequestration modelling for better prediction with limited data",
"id": "2105.04789",
"abstract": "soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the pari... | "2024-03-15T06:18:35.682065" | {
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} | [] | "algorithm" | "ae1d167d-3320-4830-87f9-279934cddf17" | 858 | medium | |
\begin{algorithmic}
\Require Time limit of each subproblem $g_i(x,\lambda)$ (e.g., 10 sec.)
\State
$S \gets \{1,\cdots, N\}$
\While{\texttt{!cut\_added}}
\For{$i$ in $S$}
\State Solve $g_i(x,\lambda)$; $\texttt{TS}_i$ $\gets$ termination status of $g_i(x,\lambda)$
\If{... | \begin{algorithmic}
\Require Time limit of each subproblem $g_i(x,\lambda)$ (e.g., 10 sec.)
\State
$S \gets \{1,\cdots, N\}$
\While{\texttt{!cut\_added}}
\For{$i$ in $S$}
\State Solve $g_i(x,\lambda)$; $\texttt{TS}_i$ $\gets$ termination status of $g_i(x,\lambda)$
\If{$\texttt{TS}_i = \texttt{OPTIMAL}$}
\State Lines 19... | "https://arxiv.org/src/2211.05903" | "2211.05903.tar.gz" | "2024-02-02" | {
"title": "two-stage distributionally robust conic linear programming over 1-wasserstein balls",
"id": "2211.05903",
"abstract": "this paper studies two-stage distributionally robust conic linear programming under constraint uncertainty over type-1 wasserstein balls. we present optimality conditions for ... | "2024-03-15T04:52:56.783142" | {
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"prof... | {
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} | [] | "algorithm" | "1c9f8775-cd50-4d9c-9ae6-c8d056ad740a" | 931 | medium | |
\begin{algorithm}
\caption{Idealized algorithm \label{alg:fake}}
\begin{algorithmic}[1]
\State Solve \eqref{intrphi} for first-order correctors $\phi_i$.
\State Determine the homogenized coefficients $a_h$ via \eqref{intrhomcoeff}.
\State Solve \eqref{intruhtilde} for $\tilde{u}_h$ on $\partial Q_L$ by $\tilde{u}_h = \... | \begin{algorithm}
\caption{Idealized algorithm }
\begin{algorithmic}
[1]
\State Solve \eqref{intrphi} for first-order correctors $\phi_i$.
\State Determine the homogenized coefficients $a_h$ via \eqref{intrhomcoeff}.
\State Solve \eqref{intruhtilde} for $\tilde{u}_h$ on $\partial Q_L$ by $\tilde{u}_h = \int G_h*(\nabla... | "https://arxiv.org/src/2109.01616" | "2109.01616.tar.gz" | "2024-01-11" | {
"title": "optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media",
"id": "2109.01616",
"abstract": "we are interested in numerical algorithms for computing the electrical field generated by a charge distribution localized on scale $\\ell$ in a... | "2024-03-15T06:22:17.156672" | {
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} | [] | "algorithm" | "80e5fb75-33cd-4630-a116-698b32bf731d" | 649 | easy | |
\begin{algorithmic}
\Require $\theta_0, m > 0, m' > 0$
\For{$k = 0,...$}
\If{$\ddot{F}(\theta_k) \succcurlyeq 0$}
\State $s_k' = 0$
\Else
\State $s_k' = $ \Call{SelectDirection()}{}
\EndIf
\If{$\dot{F}(\theta_k) = 0$}
... | \begin{algorithmic}
\Require $\theta_0, m > 0, m' > 0$
\For{$k = 0,...$}
\If{$\ddot{F}(\theta_k) \succcurlyeq 0$}
\State $s_k' = 0$
\Else
\State $s_k' = $ \Call{SelectDirection()}{}
\EndIf
\If{$\dot{F}(\theta_k) = 0$}
\State $s_k = 0$
\Else
\State $s_k = -\dot{F}(\theta_k)$
\EndIf
\If{$s_k = s_k' = 0$}
\State \Return{$... | "https://arxiv.org/src/2309.10894" | "2309.10894.tar.gz" | "2024-02-15" | {
"title": "a novel gradient methodology with economical objective function evaluations for data science applications",
"id": "2309.10894",
"abstract": "gradient methods are experiencing a growth in methodological and theoretical developments owing to the challenges of optimization problems arising in dat... | "2024-03-15T05:23:50.845023" | {
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} | [] | "algorithm" | "b6d8b1b3-9db6-4497-93b6-55f66fadd2dd" | 414 | easy | |
\begin{algorithmic}[1]
\For{$variant$ in variants}
\State dates $\gets$ unique dates in which $variant$ exists
\State dates.sort()
\State model\_initial\_weights $\gets$ random
\For{$d$ in dates}
\State retro\_data $\gets$ all data before $d$
\State processed\_data $\gets$ preproc... | \begin{algorithmic}
[1]
\For{$variant$ in variants}
\State dates $\gets$ unique dates in which $variant$ exists
\State dates.sort()
\State model\_initial\_weights $\gets$ random
\For{$d$ in dates}
\State retro\_data $\gets$ all data before $d$
\State processed\_data $\gets$ preprocess $retro\_data$
\State dataset $\get... | "https://arxiv.org/src/2401.03390" | "2401.03390.tar.gz" | "2024-01-07" | {
"title": "global prediction of covid-19 variant emergence using dynamics-informed graph neural networks",
"id": "2401.03390",
"abstract": "during the covid-19 pandemic, a major driver of new surges has been the emergence of new variants. when a new variant emerges in one or more countries, other nations... | "2024-03-15T07:51:28.358213" | {
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} | [] | "algorithm" | "7e531cdb-879f-49b5-9344-6bf1711b98d9" | 935 | medium | |
\begin{algorithm} [tb!]
\footnotesize
\caption{\small {\sf HuGE-D} walking procedure}
\label{HuGE-D_walk}
\begin{algorithmic}[1]
\Require current node $u$, candidate node $v$, Walker $W$, {\sf HuGE} parameter $\mu$
\Ensure walker state updates
{\flushleft{{\bf sendStateQuery($u$, $v$, $W$)}}} %//{submit the walker-to-v... | \begin{algorithm}
[tb!]
\footnotesize
\caption{\small {\sf HuGE-D} walking procedure}
\begin{algorithmic}
[1]
\Require current node $u$, candidate node $v$, Walker $W$, {\sf HuGE} parameter $\mu$
\Ensure walker state updates
{\flushleft{{\bf sendStateQuery($u$, $v$, $W$)}}} %//{submit the walker-to-vertex query message... | "https://arxiv.org/src/2303.15702" | "2303.15702.tar.gz" | "2024-02-25" | {
"title": "distributed graph embedding with information-oriented random walks",
"id": "2303.15702",
"abstract": "graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. the increasing availability of billion-edge graphs underscores the importance of lea... | "2024-03-15T03:43:03.810720" | {
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} | [] | "algorithm" | "2a3afe1a-0255-410b-8d3d-06d52db8df53" | 1223 | hard | |
\begin{algorithm}[t]
\caption{EM-like procedure of the proposed ADGCN}
\label{algorithm3}
\footnotesize
\begin{algorithmic}[1] %ÿÐÐÏÔʾÐкÅ
\Require
$\mathcal{G} = \{V, E\} $, $\mathbf{X}$, $A$.
\Ensure $\mathbf{H}$: the node disentangled representation matrix of the given graph
\renewcomman... | \begin{algorithm}
[t]
\caption{EM-like procedure of the proposed ADGCN}
\footnotesize
\begin{algorithmic}[1]
%ÿÐÐÏÔʾÐкÅ
\Require
$\mathcal{G} = \{V, E\} $, $\mathbf{X}$, $A$.
\Ensure $\mathbf{H}$: the node disentangled representation matrix of the given graph
\renewcommand{\algorithmicensure}{\textbf{Hyper-paramters... | "https://arxiv.org/src/2103.07295" | "2103.07295.tar.gz" | "2024-01-24" | {
"title": "adversarial graph disentanglement",
"id": "2103.07295",
"abstract": "a real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. however, most existing methods lack consideration of the intrinsic differences in relations between n... | "2024-03-15T08:52:54.851311" | {
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} | [] | "algorithm" | "9f7e356c-96f5-4c30-ad81-d17283d8b305" | 1397 | hard | |
\begin{algorithmic}[1]
\Procedure{AdaptiveEstimation}{$n$, $p_0$, $N$, $a_{LW}$, $t_{RS}$}
\State draw $\lbrace x_k \rbrace$ from $p_0(x)$
\State $\lbrace \omega_k \rbrace \gets \lbrace 1/K \rbrace$
\For{$i \in 1.. N$}
\State $\hat{T}_{\chi} \gets \sum_j \omega_k \cdot x_k$
\State... | \begin{algorithmic}
[1]
\Procedure{AdaptiveEstimation}{$n$, $p_0$, $N$, $a_{LW}$, $t_{RS}$}
\State draw $\lbrace x_k \rbrace$ from $p_0(x)$
\State $\lbrace \omega_k \rbrace \gets \lbrace 1/K \rbrace$
\For{$i \in 1.. N$}
\State $\hat{T}_{\chi} \gets \sum_j \omega_k \cdot x_k$
\State $\tau \gets \xi \cdot \hat{T}_{\chi}$... | "https://arxiv.org/src/2210.06103" | "2210.06103.tar.gz" | "2024-01-24" | {
"title": "real-time adaptive estimation of decoherence timescales for a single qubit",
"id": "2210.06103",
"abstract": "characterising the time over which quantum coherence survives is critical for any implementation of quantum bits, memories and sensors. the usual method for determining a quantum syste... | "2024-03-15T03:45:29.808933" | {
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} | [] | "algorithm" | "41d685a5-3acd-4c17-9dcc-0f4631dcc050" | 1273 | hard | |
\begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_v^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{x}_v), \forall v \in V$
\State $d_p^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{y}_p), \forall p \in P$
\Repeat \ ($\e... | \begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_v^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{x}_v), \forall v \in V$
\State $d_p^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{y}_p), \forall p \in P$
\Repeat \ ($\ell=1,2,\cdots$)
... | "https://arxiv.org/src/2206.02059" | "2206.02059.tar.gz" | "2024-01-23" | {
"title": "empowering gnns via edge-aware weisfeiler-leman algorithm",
"id": "2206.02059",
"abstract": "message passing graph neural networks (gnns) are known to have their expressiveness upper-bounded by 1-dimensional weisfeiler-leman (1-wl) algorithm. to achieve more powerful gnns, existing attempts eith... | "2024-03-15T09:04:06.314342" | {
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} | [] | "algorithm" | "2b50446c-c04b-43c2-83e3-c766cf13bf7e" | 1062 | medium | |
\begin{algorithmic}[1]
\Require $x_0\in K_0 \in\mathcal{K}_h(\Omega)$ and $x_1$ vertices of a 1-simplex $e$.
\Ensure Number of elements $N$, elements $\{K_0,..,K_{N-1}\}\in\mathcal{K}_h(\Omega)^N$.
\State $K\gets K_0$
\State $F_{\text{old}}\gets $ NULL
\State $K_{\text{old}}\gets $ NULL
\State $N \gets 1$
\State $E \g... | \begin{algorithmic}
[1]
\Require $x_0\in K_0 \in\mathcal{K}_h(\Omega)$ and $x_1$ vertices of a 1-simplex $e$.
\Ensure Number of elements $N$, elements $\{K_0,..,K_{N-1}\}\in\mathcal{K}_h(\Omega)^N$.
\State $K\gets K_0$
\State $F_{\text{old}}\gets $ NULL
\State $K_{\text{old}}\gets $ NULL
\State $N \gets 1$
\State $E \g... | "https://arxiv.org/src/2301.04923" | "2301.04923.tar.gz" | "2024-02-02" | {
"title": "semi-lagrangian finite-element exterior calculus for incompressible flows",
"id": "2301.04923",
"abstract": "we develop a mesh-based semi-lagrangian discretization of the time-dependent incompressible navier-stokes equations with free boundary conditions recast as a non-linear transport proble... | "2024-03-15T04:54:39.959037" | {
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} | [] | "algorithm" | "c04d749a-3b62-4631-9bcf-7f56ea5f76ae" | 784 | medium | |
\begin{algorithmic}[1]
\State Set the step-size $(\alpha_k)_{k=0}^\infty$, and initialize $(\theta _0,\lambda_0 )$.
\For{$k \in \{0,\ldots\}$}
\State Observe $s_k \sim d^{\beta}$, $a_k \sim \beta(\cdot|s_k)$, and $s_k'\sim P(\cdot | s_k,a_k)$, $r_k :=r(s_k,a_k,s_k')$.
\State Update parameters according to
\begin{... | \begin{algorithmic}
[1]
\State Set the step-size $(\alpha_k)_{k=0}^\infty$, and initialize $(\theta _0,\lambda_0 )$.
\For{$k \in \{0,\ldots\}$}
\State Observe $s_k \sim d^{\beta}$, $a_k \sim \beta(\cdot|s_k)$, and $s_k'\sim P(\cdot | s_k,a_k)$, $r_k :=r(s_k,a_k,s_k')$.
\State Update parameters according to
\begin{align... | "https://arxiv.org/src/2109.04033" | "2109.04033.tar.gz" | "2024-01-22" | {
"title": "new versions of gradient temporal difference learning",
"id": "2109.04033",
"abstract": "sutton, szepesv\\'{a}ri and maei introduced the first gradient temporal-difference (gtd) learning algorithms compatible with both linear function approximation and off-policy training. the goal of this paper... | "2024-03-15T09:08:57.627292" | {
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\begin{algorithm}[t]
\caption{Transfer learning algorithm for tabular contextual multi-armed bandits}
\label{alg:UCB-TL-tabular}
\begin{algorithmic}[1]
\State{\textbf{Input:} set of arms $\mathcal{I}$, horizon length $n_{Q}$, $P$-data $\mathcal{D}^{P}$.}
\For{$s \in \mathcal{S}$}
\State{Initialize the policy $\wideti... | \begin{algorithm}
[t]
\caption{Transfer learning algorithm for tabular contextual multi-armed bandits}
\begin{algorithmic}
[1]
\State{\textbf{Input:} set of arms $\mathcal{I}$, horizon length $n_{Q}$, $P$-data $\mathcal{D}^{P}$.}
\For{$s \in \mathcal{S}$}
\State{Initialize the policy $\widetilde{\pi}(s)$ by Procedure~\... | "https://arxiv.org/src/2211.12612" | "2211.12612.tar.gz" | "2024-01-24" | {
"title": "transfer learning for contextual multi-armed bandits",
"id": "2211.12612",
"abstract": "motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, where we have data collected on ... | "2024-03-15T08:57:26.780784" | {
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} | [] | "algorithm" | "5a749169-3644-439f-8884-291269657cea" | 867 | medium | |
\begin{algorithm}[ht]
\caption{Correlated pseudo-marginal algorithm}\label{CPMalgorithm}
\begin{algorithmic}[1]
\State Initialise $\boldsymbol{\theta}_0$;
\For {$m = 1,...,\textit{M}^*$}
\State Sample $\boldsymbol{\theta}^{*} \sim Q(.\vert \boldsymbol{\theta}_{m-1})$;
\State Sample $\xi \sim N(\textbf{0}, \boldsymbol{I... | \begin{algorithm}
[ht]
\caption{Correlated pseudo-marginal algorithm}\begin{algorithmic}
[1]
\State Initialise $\boldsymbol{\theta}_0$;
\For {$m = 1,...,\textit{M}^*$}
\State Sample $\boldsymbol{\theta}^{*} \sim Q(.\vert \boldsymbol{\theta}_{m-1})$;
\State Sample $\xi \sim N(\textbf{0}, \boldsymbol{I})$ and set $U^* = ... | "https://arxiv.org/src/2105.04789" | "2105.04789.tar.gz" | "2024-02-10" | {
"title": "innovative approaches in soil carbon sequestration modelling for better prediction with limited data",
"id": "2105.04789",
"abstract": "soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the pari... | "2024-03-15T06:18:35.682065" | {
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} | [] | "algorithm" | "1c15adea-7ae9-43cd-805e-50ca6b7d14d4" | 996 | medium | |
\begin{algorithm}
\caption{sPCA}
\begin{algorithmic}[1]
\State \textbf{Input}: matrix observations $\{\mathbf{X}_t\}_{t=1}^T$, factor numbers $k_1$ and $k_2$.
\State Estimate loading matrices by equations \eqref{estimator_R} and \eqref{estimator_C}.
\State Estimate factor matrices and the signal part by equations \eqre... | \begin{algorithm}
\caption{sPCA}
\begin{algorithmic}
[1]
\State \textbf{Input}: matrix observations $\{\mathbf{X}_t\}_{t=1}^T$, factor numbers $k_1$ and $k_2$.
\State Estimate loading matrices by equations \eqref{estimator_R} and \eqref{estimator_C}.
\State Estimate factor matrices and the signal part by equations \eqr... | "https://arxiv.org/src/2209.14846" | "2209.14846.tar.gz" | "2024-02-12" | {
"title": "modeling and learning on high-dimensional matrix-variate sequences",
"id": "2209.14846",
"abstract": "we propose a new matrix factor model, named radfam, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor model for each b... | "2024-03-15T05:49:17.008547" | {
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} | [] | "algorithm" | "6179e980-9b67-4da1-bd64-c6c6aec5351c" | 646 | easy | |
\begin{algorithm}[htpb]
\caption{Randomized $r$-sets-Douglas-Rachford (RrDR) method \label{r-RDRK}}
\begin{algorithmic}
\Require
$A\in \mathbb{R}^{m\times n}$, $b\in \mathbb{R}^m$, $r\in\mathbb{Z}_{+}$, $k=0$, extrapolation/relaxation parameter $\alpha\in(0,1)$ and an initial $x^0\in \mathbb{R}^{n}$.
\begin{en... | \begin{algorithm}
[htpb]
\caption{Randomized $r$-sets-Douglas-Rachford (RrDR) method }
\begin{algorithmic}
\Require
$A\in \mathbb{R}^{m\times n}$, $b\in \mathbb{R}^m$, $r\in\mathbb{Z}_{+}$, $k=0$, extrapolation/relaxation parameter $\alpha\in(0,1)$ and an initial $x^0\in \mathbb{R}^{n}$.
\begin{enumerate}
\item[1:] Set... | "https://arxiv.org/src/2207.04291" | "2207.04291.tar.gz" | "2024-01-09" | {
"title": "randomized douglas-rachford methods for linear systems: improved accuracy and efficiency",
"id": "2207.04291",
"abstract": "the douglas-rachford (dr) method is a widely used method for finding a point in the intersection of two closed convex sets (feasibility problem). however, the method conv... | "2024-03-15T06:41:30.719861" | {
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} | [] | "algorithm" | "f374ee2b-64e3-4e75-a4c7-4b2629af9fb0" | 994 | medium | |
\begin{algorithmic}[1]
\State $\mathcal{D}^{(DD)} \gets \left\{ \right\}$
\For{$i$ s.t. $\mathbf{Y}_i=1$}
\For{$j \in \mathcal{P}^{(DND)}$}
\If{$\mathbf{X}_{i,j}=1$ and $\mathbf{X}_{i,j'}=0,$ $\forall j' \neq j\in \mathcal{P}^{(DND)}$}
\State $\mathca... | \begin{algorithmic}
[1]
\State $\mathcal{D}^{(DD)} \gets \left\{ \right\}$
\For{$i$ s.t. $\mathbf{Y}_i=1$}
\For{$j \in \mathcal{P}^{(DND)}$}
\If{$\mathbf{X}_{i,j}=1$ and $\mathbf{X}_{i,j'}=0,$ $\forall j' \neq j\in \mathcal{P}^{(DND)}$}
\State $\mathcal{D}^{(DD)} \gets \mathcal{D}^{(DD)} \cup \left\{j\right\}$
\EndIf
\... | "https://arxiv.org/src/2402.10018" | "2402.10018.tar.gz" | "2024-02-15" | {
"title": "multi-stage algorithm for group testing with prior statistics",
"id": "2402.10018",
"abstract": "in this paper, we propose an efficient multi-stage algorithm for non-adaptive group testing (gt) with general correlated prior statistics. the proposed solution can be applied to any correlated stati... | "2024-03-15T04:20:09.862331" | {
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} | [] | "algorithm" | "be1745d6-9739-47fa-aceb-73f91edde679" | 388 | easy | |
\begin{algorithm}
\caption{MARK and REFINE for the time-stepping approach}
\begin{algorithmic}
\Require indicators on each interval $I_m$ and equilibration factor $c>0$
\State Calculate global temporal estimator
$\eta_k= \frac12 \sum\limits_{m=1}^M (\eta_\star^m+ \eta_\star^{m,*})$
\State Calculate global spatial esti... | \begin{algorithm}
\caption{MARK and REFINE for the time-stepping approach}
\begin{algorithmic}
\Require indicators on each interval $I_m$ and equilibration factor $c>0$
\State Calculate global temporal estimator
$\eta_k= \frac12 \sum\limits_{m=1}^M (\eta_\star^m+ \eta_\star^{m,*})$
\State Calculate global spatial estim... | "https://arxiv.org/src/2207.04764" | "2207.04764.tar.gz" | "2024-02-04" | {
"title": "numerical modeling and open-source implementation of variational partition-of-unity localizations of space-time dual-weighted residual estimators for parabolic problems",
"id": "2207.04764",
"abstract": "in this work, we consider space-time goal-oriented a posteriori error estimation for par... | "2024-03-15T07:54:29.015400" | {
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} | [] | "algorithm" | "2c4998fb-e078-4069-b9db-64c9c4e0143f" | 869 | medium | |
\begin{algorithm}
\caption{Construction of a random network from a degree sequence with configuration model}
\label{alg: Config}
Suppose we want to generate a realization of a finite graph with $N$ vertices and a given degree sequence $\textbf{d}=(d_1,d_2,...,d_N)$.
\begin{enumerate}
\item Following the algorithm des... | \begin{algorithm}
\caption{Construction of a random network from a degree sequence with configuration model}
Suppose we want to generate a realization of a finite graph with $N$ vertices and a given degree sequence $\textbf{d}=(d_1,d_2,...,d_N)$.
\begin{enumerate}
\item Following the algorithm described by \cite{Newman... | "https://arxiv.org/src/2401.06872" | "2401.06872.tar.gz" | "2024-01-12" | {
"title": "disease transmission on random graphs using edge-based percolation",
"id": "2401.06872",
"abstract": "edge-based percolation methods can be used to analyze disease transmission on complex social networks. this allows us to include complex social heterogeneity in our models while maintaining trac... | "2024-03-15T07:30:11.562367" | {
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} | [] | "algorithm" | "78832468-e3fe-48f5-83ac-0682df6e2705" | 1439 | hard | |
\begin{algorithmic}
\Require Sequence reads $X_i = \{X_0, X_1, \ldots X_N\}, \forall X_i \in \mathcal{X}_i$
\Ensure Pseudo-image $I_r$
\Procedure{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\State $e_{i,j} \gets I_r[i,j]$ \Comment{Current co-occurrence frequency}
\State $e^\prime_{i,j} \gets e_{i,j} + 1$ \Comment{New co-o... | \begin{algorithmic}
\Require Sequence reads $X_i = \{X_0, X_1, \ldots X_N\}, \forall X_i \in \mathcal{X}_i$
\Ensure Pseudo-image $I_r$
\Procedure{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\State $e_{i,j} \gets I_r[i,j]$ \Comment{Current co-occurrence frequency}
\State $e^\prime_{i,j} \gets e_{i,j} + 1$ \Comment{New co-o... | "https://arxiv.org/src/2401.13219" | "2401.13219.tar.gz" | "2024-01-23" | {
"title": "tepi: taxonomy-aware embedding and pseudo-imaging for scarcely-labeled zero-shot genome classification",
"id": "2401.13219",
"abstract": "a species' genetic code or genome encodes valuable evolutionary, biological, and phylogenetic information that aids in species recognition, taxonomic classi... | "2024-03-15T06:48:41.356948" | {
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} | [] | "algorithm" | "89d120bd-adad-4b31-9ab5-92cc3252c676" | 1353 | hard | |
\begin{algorithm}
Smoothed Probabilities
\end{algorithm}
| \begin{algorithm}
Smoothed Probabilities
\end{algorithm} | "https://arxiv.org/src/2402.08051" | "2402.08051.tar.gz" | "2024-02-12" | {
"title": "on bayesian filtering for markov regime switching models",
"id": "2402.08051",
"abstract": "this paper presents a framework for empirical analysis of dynamic macroeconomic models using bayesian filtering, with a specific focus on the state-space formulation of dynamic stochastic general equilibr... | "2024-03-15T04:21:14.605813" | {
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} | [] | "algorithm" | "d7f2dceb-4b3b-4c01-9743-7cc67bc8d389" | 56 | easy | |
\begin{algorithmic}
\State $PA\_set$
\For { OC\_block in OC\_blocks}
\State $PA\_block$
\For {$B_i \in$ OC\_block}
\State $PA\_block \gets B_i$ if $B_i \in \mathcal{F}_{a}^{PA}$
\EndFor
\State $PA\_set \gets PA\_block$
\EndFor
\end{algorithmic}
| \begin{algorithmic}
\State $PA\_set$
\For { OC\_block in OC\_blocks}
\State $PA\_block$
\For {$B_i \in$ OC\_block}
\State $PA\_block \gets B_i$ if $B_i \in \mathcal{F}_{a}^{PA}$
\EndFor
\State $PA\_set \gets PA\_block$
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2208.01756" | "2208.01756.tar.gz" | "2024-02-26" | {
"title": "permutation-adapted complete and independent basis for atomic cluster expansion descriptors",
"id": "2208.01756",
"abstract": "atomic cluster expansion (ace) methods provide a systematic way to describe particle local environments of arbitrary body order. for practical applications it is often... | "2024-03-15T02:45:54.507286" | {
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} | [] | "algorithm" | "002fbb31-bf14-4293-b495-21532297dfc8" | 244 | easy | |
\begin{algorithm}[t]
\caption{\underline{NC-1-WL} \emph{vs.} 1-WL for graph isomorphism test}
\label{alg:NC-1-WL}
\begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_v^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{x}_v), \for... | \begin{algorithm}
[t]
\caption{\underline{NC-1-WL} \emph{vs.} 1-WL for graph isomorphism test}
\begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_v^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{x}_v), \forall v \in V$
\State $d_p^{(... | "https://arxiv.org/src/2206.02059" | "2206.02059.tar.gz" | "2024-01-23" | {
"title": "empowering gnns via edge-aware weisfeiler-leman algorithm",
"id": "2206.02059",
"abstract": "message passing graph neural networks (gnns) are known to have their expressiveness upper-bounded by 1-dimensional weisfeiler-leman (1-wl) algorithm. to achieve more powerful gnns, existing attempts eith... | "2024-03-15T09:04:06.314342" | {
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} | [] | "algorithm" | "b8b7f04e-bbb0-49dd-a061-ec109b7a2c6b" | 1173 | hard | |
\begin{algorithm}
\caption{Optimal algorithm for the approximate solution $u^{(L)}$ in $Q_L$ \label{alg:truealg}}
\begin{algorithmic}[1]
\State For $\beta\in (1,\frac{3}{2})$ set $\varepsilon = \frac{3}{2}-\beta$ and $T = L^{2(1-\varepsilon)}$. For $i=1,\cdots,d$, solve for the approximate first-order corrector $\phi_{... | \begin{algorithm}
\caption{Optimal algorithm for the approximate solution $u^{(L)}$ in $Q_L$ }
\begin{algorithmic}
[1]
\State For $\beta\in (1,\frac{3}{2})$ set $\varepsilon = \frac{3}{2}-\beta$ and $T = L^{2(1-\varepsilon)}$. For $i=1,\cdots,d$, solve for the approximate first-order corrector $\phi_{i,T}^{(L)}$:
\begi... | "https://arxiv.org/src/2109.01616" | "2109.01616.tar.gz" | "2024-01-11" | {
"title": "optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media",
"id": "2109.01616",
"abstract": "we are interested in numerical algorithms for computing the electrical field generated by a charge distribution localized on scale $\\ell$ in a... | "2024-03-15T06:31:22.744786" | {
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} | [] | "algorithm" | "b92119b2-9612-4cce-88b5-02a8c89b3abb" | 3011 | hard | |
\begin{algorithm}[!htbp]
\caption{Algorithm for CPI Prediction (Multimodal)}
\label{algo:1}
\begin{algorithmic}[1]
\Require Compound Graphs $\{\mathcal{G}_C^{(j)}\}_{j=1}^M$, Protein Sequences $\{S^{(i)}\}_{i=1}^N$, and Protein Graphs $\{\mathcal{G}_{\mathcal{P}}^{(i)}\}_{i=1}^N$.
\Ensure Interaction pattern $\... | \begin{algorithm}
[!htbp]
\caption{Algorithm for CPI Prediction (Multimodal)}
\begin{algorithmic}
[1]
\Require Compound Graphs $\{\mathcal{G}_C^{(j)}\}_{j=1}^M$, Protein Sequences $\{S^{(i)}\}_{i=1}^N$, and Protein Graphs $\{\mathcal{G}_{\mathcal{P}}^{(i)}\}_{i=1}^N$.
\Ensure Interaction pattern $\mathbf{P}^{\text{cont... | "https://arxiv.org/src/2402.08198" | "2402.08198.tar.gz" | "2024-02-12" | {
"title": "psc-cpi: multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction",
"id": "2402.08198",
"abstract": "compound-protein interaction (cpi) prediction aims to predict the pattern and strength of compound-protein interactions for rat... | "2024-03-15T04:40:50.031648" | {
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} | [] | "algorithm" | "1449d316-9230-46f7-97f3-7e81e66a9874" | 1378 | hard | |
\begin{algorithmic}
\Require indicators on each interval $I_m$ and equilibration factor $c>0$
\State Calculate global temporal estimator
$\eta_k= \frac12 \sum\limits_{m=1}^M (\eta_\star^m+ \eta_\star^{m,*})$
\State Calculate global spatial estimator
$\eta_h= \frac12 \sum\limits_{m=1}^M \sum\limits_{i\in\mathcal{T}_h^... | \begin{algorithmic}
\Require indicators on each interval $I_m$ and equilibration factor $c>0$
\State Calculate global temporal estimator
$\eta_k= \frac12 \sum\limits_{m=1}^M (\eta_\star^m+ \eta_\star^{m,*})$
\State Calculate global spatial estimator
$\eta_h= \frac12 \sum\limits_{m=1}^M \sum\limits_{i\in\mathcal{T}_h^m}... | "https://arxiv.org/src/2207.04764" | "2207.04764.tar.gz" | "2024-02-04" | {
"title": "numerical modeling and open-source implementation of variational partition-of-unity localizations of space-time dual-weighted residual estimators for parabolic problems",
"id": "2207.04764",
"abstract": "in this work, we consider space-time goal-oriented a posteriori error estimation for par... | "2024-03-15T07:54:29.015400" | {
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} | [] | "algorithm" | "eed5ae79-d4d4-4888-a412-3cc8bfbca634" | 778 | medium | |
\begin{algorithm}
\caption{generalized\_sparsemax($z$, $r$)}
\begin{algorithmic}[1]
\State Sort \( z \) in decreasing order \( z_{1} \geq \dots \geq z_{c} \)
\State Find \( \kappa(z) \) such that
\[
\kappa(z) = \max_{k = 1 \ldots c} \left\{ k \, \bigg| \, r + k z_{k} > \sum_{j \leq k} z_{j} \right\}
\]
\State Define
\... | \begin{algorithm}
\caption{generalized\_sparsemax($z$, $r$)}
\begin{algorithmic}
[1]
\State Sort \( z \) in decreasing order \( z_{1} \geq \dots \geq z_{c} \)
\State Find \( \kappa(z) \) such that
\[
\kappa(z) = \max_{k = 1 \ldots c} \left\{ k \, \bigg| \, r + k z_{k} > \sum_{j \leq k} z_{j} \right\}
\]
\State Define
\... | "https://arxiv.org/src/2309.16883" | "2309.16883.tar.gz" | "2024-02-06" | {
"title": "the lipschitz-variance-margin tradeoff for enhanced randomized smoothing",
"id": "2309.16883",
"abstract": "real-life applications of deep neural networks are hindered by their unsteady predictions when faced with noisy inputs and adversarial attacks. the certified radius is in this context a cr... | "2024-03-15T07:05:43.113842" | {
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} | [] | "algorithm" | "f23724b9-1ad1-4f63-a8a3-06ed5c6daaad" | 507 | easy | |
\begin{algorithm}{({\bf DR})}\label{alg:DR}
\begin{description}
\item[Step 1] ({\em Initialization}) Choose a parameter $\lambda\in\left]0,1\right[$ and the initial iterate $u^0$ arbitrarily.
Choose a small parameter $\varepsilon>0$, and set $k=0$.
\item[Step 2] ({\em Projection onto ${\cal B}$}) Set $u^- = \lambda ... | \begin{algorithm}
{({\bf DR})}\begin{description}
\item[Step 1] ({\em Initialization}) Choose a parameter $\lambda\in\left]0,1\right[$ and the initial iterate $u^0$ arbitrarily.
Choose a small parameter $\varepsilon>0$, and set $k=0$.
\item[Step 2] ({\em Projection onto ${\cal B}$}) Set $u^- = \lambda u^{k}$.
Compute $... | "https://arxiv.org/src/2210.17279" | "2210.17279.tar.gz" | "2024-01-11" | {
"title": "douglas--rachford algorithm for control-constrained minimum-energy control problems",
"id": "2210.17279",
"abstract": "splitting and projection-type algorithms have been applied to many optimization problems due to their simplicity and efficiency, but the application of these algorithms to opt... | "2024-03-15T06:24:12.690702" | {
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} | [] | "algorithm" | "bdf7c5dd-81f6-42a9-ab9e-33997175cc18" | 852 | medium | |
\begin{algorithm}
\caption{K-folds cross-validation BidNet training procedure}
\begin{algorithmic}[1]
\State $D \gets \{D_1,\dots,D_K\}$ \Comment{Initialize K-folds}
\State $loss^{*}\gets \infty$ \Comment{Initialize best model}
\For {$fold \in D$}
\State $reset(w_{BidNet})$ \Comm... | \begin{algorithm}
\caption{K-folds cross-validation BidNet training procedure}
\begin{algorithmic}
[1]
\State $D \gets \{D_1,\dots,D_K\}$ \Comment{Initialize K-folds}
\State $loss^{*}\gets \infty$ \Comment{Initialize best model}
\For {$fold \in D$}
\State $reset(w_{BidNet})$ \Comment{Reset parameters before entering ea... | "https://arxiv.org/src/2207.12255" | "2207.12255.tar.gz" | "2024-02-15" | {
"title": "implementing a hierarchical deep learning approach for simulating multi-level auction data",
"id": "2207.12255",
"abstract": "we present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. the complexities encountered in thi... | "2024-03-15T04:09:20.272119" | {
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} | [] | "algorithm" | "17d4903b-04cf-4df4-82c3-3b34ea656b4d" | 1074 | medium | |
\begin{algorithmic}[1]
\State Sort \( z \) in decreasing order \( z_{1} \geq \dots \geq z_{c} \)
\State Find \( \kappa(z) \) such that
\[
\kappa(z) = \max_{k = 1 \ldots c} \left\{ k \, \bigg| \, r + k z_{k} > \sum_{j \leq k} z_{j} \right\}
\]
\State Define
\[
\rho(z) = \frac{\left(\sum_{j \leq \kappa(z)} z_j\right) - ... | \begin{algorithmic}
[1]
\State Sort \( z \) in decreasing order \( z_{1} \geq \dots \geq z_{c} \)
\State Find \( \kappa(z) \) such that
\[
\kappa(z) = \max_{k = 1 \ldots c} \left\{ k \, \bigg| \, r + k z_{k} > \sum_{j \leq k} z_{j} \right\}
\]
\State Define
\[
\rho(z) = \frac{\left(\sum_{j \leq \kappa(z)} z_j\right) - ... | "https://arxiv.org/src/2309.16883" | "2309.16883.tar.gz" | "2024-02-06" | {
"title": "the lipschitz-variance-margin tradeoff for enhanced randomized smoothing",
"id": "2309.16883",
"abstract": "real-life applications of deep neural networks are hindered by their unsteady predictions when faced with noisy inputs and adversarial attacks. the certified radius is in this context a cr... | "2024-03-15T07:05:43.113842" | {
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} | [] | "algorithm" | "4785c6d2-3bf1-4533-93f1-cb845da8cc8a" | 430 | easy | |
\begin{algorithmic}[1]
\Require{ A degree-$p$ piece-wise function $f_\ell(x) = \sum_{j=0}^p a_{j}^{(\ell)} x^j $. System size ${N}$. Domain [a,b]. Support bit $k$.}
\Ensure{A $\chi \le 2^k(p+1)$ MPS, $\bf{M}_T$ which encodes $f_\ell(x)$}
\Statex
\For{$\ell \gets 1$ to $2^k$}
\State {Encode $f_\ell(x)$ into ${... | \begin{algorithmic}
[1]
\Require{ A degree-$p$ piece-wise function $f_\ell(x) = \sum_{j=0}^p a_{j}^{(\ell)} x^j $. System size ${N}$. Domain [a,b]. Support bit $k$.}
\Ensure{A $\chi \le 2^k(p+1)$ MPS, $\bf{M}_T$ which encodes $f_\ell(x)$}
\Statex
\For{$\ell \gets 1$ to $2^k$}
\State {Encode $f_\ell(x)$ into ${\bf M}_\e... | "https://arxiv.org/src/2303.01562" | "2303.01562.tar.gz" | "2024-02-16" | {
"title": "quantum state preparation of normal distributions using matrix product states",
"id": "2303.01562",
"abstract": "state preparation is a necessary component of many quantum algorithms. in this work, we combine a method for efficiently representing smooth differentiable probability distributions... | "2024-03-15T04:16:05.461100" | {
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} | [] | "algorithm" | "a558a599-57f0-48b5-a1a5-916d7f9e4be3" | 496 | easy | |
\begin{algorithm}
\caption{Node subsampling/resampling bootstrap for two-sample inference}
\label{algorithm::node-resample}
{\bf Input:} Networks $A,B$; bootstrap repetition $N_{\rm boot}$; if subsampling: subsample sizes $m_{\rm sub},n_{\rm sub}$\\
{\bf Output:} Bootstrapped studentized empirical momen... | \begin{algorithm}
\caption{Node subsampling/resampling bootstrap for two-sample inference}
{\bf Input:} Networks $A,B$; bootstrap repetition $N_{\rm boot}$; if subsampling: subsample sizes $m_{\rm sub},n_{\rm sub}$\\
{\bf Output:} Bootstrapped studentized empirical moment discrepancies $\{\hat T_{m,n}^{(b)}\}_{b=1,\ldo... | "https://arxiv.org/src/2208.07573" | "2208.07573.tar.gz" | "2024-02-02" | {
"title": "higher-order accurate two-sample network inference and network hashing",
"id": "2208.07573",
"abstract": "two-sample hypothesis testing for network comparison presents many significant challenges, including: leveraging repeated network observations and known node registration, but without requir... | "2024-03-15T04:50:33.625258" | {
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} | [] | "algorithm" | "9e0f944a-a23c-4821-a3a5-2176db9c30a5" | 881 | medium | |
\begin{algorithmic}[1]
\Procedure{$\mathrm{CS}_t$}{$R, S$}
\Comment{$t$: log number of parallel control-swaps, $R$: control bit data register with at least $2^t$ qubits, $S$: target bit angle register with at least $2^{t+1}$ qubits (note that the subscript here labels the qubit indices of every \textit... | \begin{algorithmic}
[1]
\Procedure{$\mathrm{CS}_t$}{$R, S$}
\Comment{$t$: log number of parallel control-swaps, $R$: control bit data register with at least $2^t$ qubits, $S$: target bit angle register with at least $2^{t+1}$ qubits (note that the subscript here labels the qubit indices of every \textit{single} registe... | "https://arxiv.org/src/2303.02131" | "2303.02131.tar.gz" | "2024-02-09" | {
"title": "spacetime-efficient low-depth quantum state preparation with applications",
"id": "2303.02131",
"abstract": "we propose a novel deterministic method for preparing arbitrary quantum states. when our protocol is compiled into cnot and arbitrary single-qubit gates, it prepares an $n$-dimensional ... | "2024-03-15T03:55:47.804290" | {
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} | [] | "algorithm" | "a857f638-8b82-4927-9547-f7a55ec58dab" | 481 | easy | |
\begin{algorithm}
\caption{Calculate $y = x^n$}\label{algo1}
\begin{algorithmic}[1]
\Require $n \geq 0 \vee x \neq 0$
\Ensure $y = x^n$
\State $y \Leftarrow 1$
\If{$n < 0$}\label{algln2}
\State $X \Leftarrow 1 / x$
\State $N \Leftarrow -n$
\Else
\State $X \Leftarrow x$
\State $N \Leftar... | \begin{algorithm}
\caption{Calculate $y = x^n$}\begin{algorithmic}
[1]
\Require $n \geq 0 \vee x \neq 0$
\Ensure $y = x^n$
\State $y \Leftarrow 1$
\If{$n < 0$} \State $X \Leftarrow 1 / x$
\State $N \Leftarrow -n$
\Else
\State $X \Leftarrow x$
\State $N \Leftarrow n$
\EndIf
\While{$N \neq 0$}
\If{$N$ is even}
\State $X ... | "https://arxiv.org/src/2202.05650" | "2202.05650.tar.gz" | "2024-02-23" | {
"title": "bernstein flows for flexible posteriors in variational bayes",
"id": "2202.05650",
"abstract": "variational inference (vi) is a technique to approximate difficult to compute posteriors by optimization. in contrast to mcmc, vi scales to many observations. in the case of complex posteriors, howeve... | "2024-03-15T03:55:04.902331" | {
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} | [] | "algorithm" | "2df04537-61d7-47f1-a575-e25388c6720e" | 500 | easy | |
\begin{algorithmic}[1]
\State The agents gather information about the prices of the products of their providers.
\State They calculate their demand for goods(either as input for production or for consumption) and labour(applicable only for producers), leisure and income for the next period. .
\State All ... | \begin{algorithmic}
[1]
\State The agents gather information about the prices of the products of their providers.
\State They calculate their demand for goods(either as input for production or for consumption) and labour(applicable only for producers), leisure and income for the next period. .
\State All the agents sen... | "https://arxiv.org/src/2401.07070" | "2401.07070.tar.gz" | "2024-01-13" | {
"title": "a dynamic agent based model of the real economy with monopolistic competition, perfect product differentiation, heterogeneous agents, increasing returns to scale and trade in disequilibrium",
"id": "2401.07070",
"abstract": "we have used agent-based modeling as our numerical method to artifi... | "2024-03-15T06:13:08.276479" | {
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} | [] | "algorithm" | "a1612f64-29ac-47cb-81e9-e8e0362829e4" | 2341 | hard | |
\begin{algorithm}
\caption{Anytime Valid Linear Model Summary in R}
\begin{verbatim}
mod <- summary(lm(outcome ~ . + trt*., data=df))
stderrs <- mod$coefficients[, 'Std. Error']
t2 <- mod$coefficients[, 't value']^2
ols <- mod$coefficients[, 'Estimate']
nu <- nrow(df) - length(ols)
z2 <- (mod$sigma / stderrs)^2
r <- ph... | \begin{algorithm}
\caption{Anytime Valid Linear Model Summary in R}
\begin{verbatim}
mod <- summary(lm(outcome ~ . + trt*., data=df))
stderrs <- mod$coefficients[, 'Std. Error']
t2 <- mod$coefficients[, 't value']^2
ols <- mod$coefficients[, 'Estimate']
nu <- nrow(df) - length(ols)
z2 <- (mod$sigma / stderrs)^2
r <- ph... | "https://arxiv.org/src/2210.08589" | "2210.08589.tar.gz" | "2024-02-07" | {
"title": "anytime-valid linear models and regression adjusted causal inference in randomized experiments",
"id": "2210.08589",
"abstract": "linear regression adjustment is commonly used to analyse randomised controlled experiments due to its efficiency and robustness against model misspecification. curr... | "2024-03-15T04:40:18.918382" | {
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} | [] | "algorithm" | "803cc3fc-4c90-425c-9d0a-1bd111e305b3" | 639 | easy | |
\begin{algorithmic}[1]
\State preds = $f$.predict(\textit{samples})
\Comment{Applying the test data on f to get the prediction results}
\State targets= the opposite label of the preds
\Comment{Get the target labels based on the prediction results}
\For{$\textit{sample}$ $\leftarrow$ $\textit{samples}$}
\If{target(s... | \begin{algorithmic}
[1]
\State preds = $f$.predict(\textit{samples})
\Comment{Applying the test data on f to get the prediction results}
\State targets= the opposite label of the preds
\Comment{Get the target labels based on the prediction results}
\For{$\textit{sample}$ $\leftarrow$ $\textit{samples}$}
\If{target(samp... | "https://arxiv.org/src/2211.04411" | "2211.04411.tar.gz" | "2024-02-01" | {
"title": "motif-guided time series counterfactual explanations",
"id": "2211.04411",
"abstract": "with the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. to improve th... | "2024-03-15T08:04:45.521940" | {
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} | [] | "algorithm" | "075e539e-0bde-4c85-a97e-e20874b3c03d" | 784 | medium | |
\begin{algorithm}[htb]
\caption{Training procedure of the moment-based neural Hawkes}
\label{algo:pinn_training}
\begin{enumerate}
\item
Estimate the first order statistics $\Lambda$ and second order statistics $G$ given by Equation \eqref{eq:def_G_statistics} over the time and mark domains.... | \begin{algorithm}
[htb]
\caption{Training procedure of the moment-based neural Hawkes}
\begin{enumerate}
\item
Estimate the first order statistics $\Lambda$ and second order statistics $G$ given by Equation \eqref{eq:def_G_statistics} over the time and mark domains.
\item
For $i=1$ to $D$:
\begin{enumerate}
\item
Repre... | "https://arxiv.org/src/2401.09361" | "2401.09361.tar.gz" | "2024-01-18" | {
"title": "neural hawkes: non-parametric estimation in high dimension and causality analysis in cryptocurrency markets",
"id": "2401.09361",
"abstract": "we propose a novel approach to marked hawkes kernel inference which we name the moment-based neural hawkes estimation method. hawkes processes are full... | "2024-03-15T05:59:43.122977" | {
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} | [] | "algorithm" | "3526bd02-0f50-49a9-893e-5a5a4f7c252a" | 2065 | hard | |
\begin{algorithm}[H]
\caption{D-Mapper}%标题
\label{D-Mapper}%标签
\begin{algorithmic}[1]
\State Choose a proper filter function $f$ to project data on the real line,$f: X \rightarrow \mathbb{R}$.
\State Choose a component number $n$ and quantile $\alpha$.
\State Fit projected data to a mixtu... | \begin{algorithm}
[H]
\caption{D-Mapper}%标题
%标签
\begin{algorithmic}
[1]
\State Choose a proper filter function $f$ to project data on the real line,$f: X \rightarrow \mathbb{R}$.
\State Choose a component number $n$ and quantile $\alpha$.
\State Fit projected data to a mixture model.
\For{$i$th component of the mixture... | "https://arxiv.org/src/2401.12237" | "2401.12237.tar.gz" | "2024-01-19" | {
"title": "a distribution-guided mapper algorithm",
"id": "2401.12237",
"abstract": "motivation: the mapper algorithm is an essential tool to explore shape of data in topology data analysis. with a dataset as an input, the mapper algorithm outputs a graph representing the topological features of the whole ... | "2024-03-15T06:54:42.856784" | {
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} | [] | "algorithm" | "8a95aa39-1412-4043-9baf-8bbc24ce302f" | 858 | medium | |
\begin{algorithm}
\caption{Sequential coupling for one time-step}\label{alg:stag}
$\mathbf{u}_{mesh}$ = $\left(\mathbf{x}_{n} - \mathbf{x}_{n-1} \right) \frac{1}{\Delta t}$\\
$\mathbf{u}_{n+1}$ = \text{Navier-Stokes} $(\mathbf{u}_n, \phi_n, \mathbf{u}_{mesh})$\\
$\phi_{adv}$ = \text{level set} $(\mathbf{u}_{n+1}, \phi... | \begin{algorithm}
\caption{Sequential coupling for one time-step}
$\mathbf{u}_{mesh}$ = $\left(\mathbf{x}_{n} - \mathbf{x}_{n-1} \right) \frac{1}{\Delta t}$\\
$\mathbf{u}_{n+1}$ = \text{Navier-Stokes} $(\mathbf{u}_n, \phi_n, \mathbf{u}_{mesh})$\\
$\phi_{adv}$ = \text{level set} $(\mathbf{u}_{n+1}, \phi_n)$\\
$\mathbf{x... | "https://arxiv.org/src/2302.03983" | "2302.03983.tar.gz" | "2024-02-01" | {
"title": "x-mesh: a new approach for the simulation of two-phase flow with sharp interface",
"id": "2302.03983",
"abstract": "accurate modeling of moving boundaries and interfaces is a difficulty present in many situations of computational mechanics. we use the extreme mesh deformation approach (x-mesh)... | "2024-03-15T08:12:32.496033" | {
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} | [] | "algorithm" | "d34f1e9e-0fc7-4c2b-a79d-dec77dfe6883" | 535 | easy | |
\begin{algorithm}\label{alg:sbo1}
To place the blocks of a partition in size-biased order, sample uniformly without replacement from the underlying set, then list the blocks in the order they are discovered. In other words, given $A$ and $P$, and $B(u), u\in A$ as above, let $n=|A|$ and $u:\{1,\dots,n\}\to A$ be a unif... | \begin{algorithm}
To place the blocks of a partition in size-biased order, sample uniformly without replacement from the underlying set, then list the blocks in the order they are discovered. In other words, given $A$ and $P$, and $B(u), u\in A$ as above, let $n=|A|$ and $u:\{1,\dots,n\}\to A$ be a uniform random bijec... | "https://arxiv.org/src/2104.00193" | "2104.00193.tar.gz" | "2024-01-12" | {
"title": "takeover, fixation and identifiability in finite neutral genealogy models",
"id": "2104.00193",
"abstract": "for neutral genealogy models in a finite, possibly non-constant population, there is a convenient ordered rearrangement of the particles, known as the lookdown representation, that grea... | "2024-03-15T06:08:13.487657" | {
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"num_done": {
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} | [] | "algorithm" | "e9cff77a-5306-449c-839f-f220c1fd734a" | 625 | easy | |
\begin{algorithmic}[1]
\Require{The probability of treatment assignment: $p$; a model class for the weight prediction: $\mathcal{G}=\{G_{\theta_W}: \mathbb{R}^d \rightarrow \{0,1\}, {\theta_W}\in \Theta_W\}$; the machine learning model class: $\mathcal{M}=\{M_\theta: \mathcal{X} \rightarrow \mathcal{Y} , \theta\in ... | \begin{algorithmic}
[1]
\Require{The probability of treatment assignment: $p$; a model class for the weight prediction: $\mathcal{G}=\{G_{\theta_W}: \mathbb{R}^d \rightarrow \{0,1\}, {\theta_W}\in \Theta_W\}$; the machine learning model class: $\mathcal{M}=\{M_\theta: \mathcal{X} \rightarrow \mathcal{Y} , \theta\in \Th... | "https://arxiv.org/src/2310.17496" | "2310.17496.tar.gz" | "2024-02-03" | {
"title": "tackling interference induced by data training loops in a/b tests: a weighted training approach",
"id": "2310.17496",
"abstract": "in modern recommendation systems, the standard pipeline involves training machine learning models on historical data to predict user behaviors and improve recommen... | "2024-03-15T04:57:23.023908" | {
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} | [] | "algorithm" | "a782dcb8-0763-4306-9476-e7f1910425a2" | 1590 | hard | |
\begin{algorithm}
\caption{Training Step for Model $f_k$ (with parameters $\theta_k$) using decorrelation. $\lambda$ and $r$ are hyperparameters.}\label{alg:dec_alg}
\begin{algorithmic}
\State Draw $X_b, Y_b, [Z_{k-1,b} \cdots Z_{0,b}]$ \Comment{Draw training batch and corresponding features from prior models}
\State $... | \begin{algorithm}
\caption{Training Step for Model $f_k$ (with parameters $\theta_k$) using decorrelation. $\lambda$ and $r$ are hyperparameters.}\begin{algorithmic}
\State Draw $X_b, Y_b, [Z_{k-1,b} \cdots Z_{0,b}]$ \Comment{Draw training batch and corresponding features from prior models}
\State $Z_{k,b} \gets f^l_k(... | "https://arxiv.org/src/2207.09031" | "2207.09031.tar.gz" | "2024-02-16" | {
"title": "decorrelative network architecture for robust electrocardiogram classification",
"id": "2207.09031",
"abstract": "artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed. as it is... | "2024-03-15T04:53:44.109126" | {
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} | [] | "algorithm" | "e48ab977-e08a-4350-beaf-9c7f7a5b1929" | 982 | medium | |
\begin{algorithmic}
\Require{($\hat{x}_k,\hat{u}_k\,\hat{Q}_k,\hat{K}_k$)}
\For{$i=1\ldots N_{max}$}
\State{optimize $\bar{x}_k,\bar{u}_k$ by \eqref{eq:traj_update}}
\State{estimate $\gamma_k$ via \eqref{eq:gamma_update} or \eqref{eq:approximate_outer_optimization}}
\State{optimize $Q_k,K_k$ by \eqref{eq:funne... | \begin{algorithmic}
\Require{($\hat{x}_k,\hat{u}_k\,\hat{Q}_k,\hat{K}_k$)}
\For{$i=1\ldots N_{max}$}
\State{optimize $\bar{x}_k,\bar{u}_k$ by \eqref{eq:traj_update}}
\State{estimate $\gamma_k$ via \eqref{eq:gamma_update} or \eqref{eq:approximate_outer_optimization}}
\State{optimize $Q_k,K_k$ by \eqref{eq:funnel_update}... | "https://arxiv.org/src/2209.03535" | "2209.03535.tar.gz" | "2024-01-12" | {
"title": "joint synthesis of trajectory and controlled invariant funnel for discrete-time systems with locally lipschitz nonlinearities",
"id": "2209.03535",
"abstract": "this paper presents a joint synthesis algorithm of trajectory and controlled invariant funnel (cif) for locally lipschitz nonlinear s... | "2024-03-15T06:18:49.073482" | {
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} | [] | "algorithm" | "0ac2d7f9-a8c4-45cb-b460-cf280df12e6f" | 552 | easy | |
\begin{algorithmic}
\State \textbf{\textit{Initialization of the set of time steps}}
\State $T_{div} \gets {t \in T_{m} \mkern9mu | \mkern9mu bn_{t} > 0}$\\
\State \textbf{\textit{Initialization of the first slice $i = 1$}}
\State $V_{1} \gets \min(V^{s}, \max\limits_{t \in ... | \begin{algorithmic}
\State \textbf{\textit{Initialization of the set of time steps}}
\State $T_{div} \gets {t \in T_{m} \mkern9mu | \mkern9mu bn_{t} > 0}$\\
\State \textbf{\textit{Initialization of the first slice $i = 1$}}
\State $V_{1} \gets \min(V^{s}, \max\limits_{t \in T_{div}} bn_{t})$\\
\State \textbf{\textit{Re... | "https://arxiv.org/src/2402.12859" | "2402.12859.tar.gz" | "2024-02-20" | {
"title": "atlas: a model of short-term european electricity market processes under uncertainty -- balancing modules",
"id": "2402.12859",
"abstract": "the atlas model simulates the various stages of the electricity market chain in europe, including the formulation of offers by different market actors, t... | "2024-03-15T03:21:13.620616" | {
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} | [] | "algorithm" | "9fae7478-ae9e-44ed-bead-b393c41bed37" | 990 | medium |
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