| A Characterization of Multioutput Learnability |
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1 |
| A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent |
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1 |
| A Data-Adaptive RKHS Prior for Bayesian Learning of Kernels in Operators |
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1 |
| A Framework for Improving the Reliability of Black-box Variational Inference |
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4 |
| A General Framework for the Analysis of Kernel-based Tests |
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0 |
| A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment |
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3 |
| A Multilabel Classification Framework for Approximate Nearest Neighbor Search |
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7 |
| A New, Physics-Informed Continuous-Time Reinforcement Learning Algorithm with Performance Guarantees |
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7 |
| A Note on Entrywise Consistency for Mixed-data Matrix Completion |
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5 |
| A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks |
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3 |
| A Rainbow in Deep Network Black Boxes |
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4 |
| A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness |
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7 |
| A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables |
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4 |
| A Statistical Experimental Design Method for Constructing Deterministic Sensing Matrices for Compressed Sensing |
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2 |
| A Survey on Multi-player Bandits |
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1 |
| A Variational Approach to Bayesian Phylogenetic Inference |
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6 |
| A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression |
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7 |
| A minimax optimal approach to high-dimensional double sparse linear regression |
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6 |
| A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity |
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3 |
| A tensor factorization model of multilayer network interdependence |
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5 |
| AMLB: an AutoML Benchmark |
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6 |
| Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization |
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3 |
| Accelerating Nuclear-norm Regularized Low-rank Matrix Optimization Through Burer-Monteiro Decomposition |
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6 |
| Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees |
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5 |
| Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction |
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4 |
| Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization |
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4 |
| Additive smoothing error in backward variational inference for general state-space models |
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3 |
| Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning |
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1 |
| Aequitas Flow: Streamlining Fair ML Experimentation |
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2 |
| Almost Sure Convergence Rates Analysis and Saddle Avoidance of Stochastic Gradient Methods |
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1 |
| An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization |
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1 |
| An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters |
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6 |
| An Analysis of Quantile Temporal-Difference Learning |
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2 |
| An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants |
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❌ |
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5 |
| An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates |
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0 |
| An Entropy-Based Model for Hierarchical Learning |
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1 |
| An Inexact Projected Regularized Newton Method for Fused Zero-norms Regularization Problems |
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6 |
| An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification |
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4 |
| Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC |
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6 |
| Approximate Information Tests on Statistical Submanifolds |
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2 |
| Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models |
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5 |
| Axiomatic effect propagation in structural causal models |
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4 |
| Bagging Provides Assumption-free Stability |
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3 |
| Bayesian Regression Markets |
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5 |
| Bayesian Structural Learning with Parametric Marginals for Count Data: An Application to Microbiota Systems |
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4 |
| BenchMARL: Benchmarking Multi-Agent Reinforcement Learning |
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2 |
| Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box |
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❌ |
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4 |
| Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria |
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1 |
| Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations |
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5 |
| Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds |
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2 |
| Causal Discovery with Generalized Linear Models through Peeling Algorithms |
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5 |
| Causal effects of intervening variables in settings with unmeasured confounding |
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2 |
| Causal-learn: Causal Discovery in Python |
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2 |
| Characterization of translation invariant MMD on Rd and connections with Wasserstein distances |
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1 |
| Choosing the Number of Topics in LDA Models – A Monte Carlo Comparison of Selection Criteria |
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3 |
| Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks |
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0 |
| Classification with Deep Neural Networks and Logistic Loss |
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0 |
| Cluster-Adaptive Network A/B Testing: From Randomization to Estimation |
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3 |
| Commutative Scaling of Width and Depth in Deep Neural Networks |
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1 |
| Compressed and distributed least-squares regression: convergence rates with applications to federated learning |
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4 |
| Concentration and Moment Inequalities for General Functions of Independent Random Variables with Heavy Tails |
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❌ |
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0 |
| Conformal Inference for Online Prediction with Arbitrary Distribution Shifts |
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✅ |
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❌ |
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✅ |
5 |
| Consistent Multiclass Algorithms for Complex Metrics and Constraints |
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5 |
| Contamination-source based K-sample clustering |
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4 |
| Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression |
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1 |
| Continuous Prediction with Experts' Advice |
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0 |
| Convergence for nonconvex ADMM, with applications to CT imaging |
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3 |
| Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs |
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2 |
| Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations" |
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0 |
| Countering the Communication Bottleneck in Federated Learning: A Highly Efficient Zero-Order Optimization Technique |
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❌ |
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✅ |
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4 |
| Critically Assessing the State of the Art in Neural Network Verification |
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4 |
| DAG-Informed Structure Learning from Multi-Dimensional Point Processes |
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4 |
| Data Summarization via Bilevel Optimization |
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5 |
| Data Thinning for Convolution-Closed Distributions |
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5 |
| Data-Efficient Policy Evaluation Through Behavior Policy Search |
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3 |
| Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls |
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4 |
| Debiasing Evaluations That Are Biased by Evaluations |
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5 |
| Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning |
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4 |
| Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics |
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6 |
| Decomposing Global Feature Effects Based on Feature Interactions |
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5 |
| Decorrelated Variable Importance |
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1 |
| Deep Backward and Galerkin Methods for the Finite State Master Equation |
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✅ |
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5 |
| Deep Network Approximation: Beyond ReLU to Diverse Activation Functions |
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0 |
| Deep Neural Network Approximation of Invariant Functions through Dynamical Systems |
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0 |
| Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces |
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0 |
| Deep Nonparametric Quantile Regression under Covariate Shift |
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3 |
| Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization |
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4 |
| Desiderata for Representation Learning: A Causal Perspective |
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5 |
| Differentially Private Data Release for Mixed-type Data via Latent Factor Models |
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4 |
| Differentially Private Topological Data Analysis |
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4 |
| Differentially private methods for managing model uncertainty in linear regression |
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3 |
| Distributed Estimation on Semi-Supervised Generalized Linear Model |
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4 |
| Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms |
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1 |
| Distributed Kernel-Driven Data Clustering |
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3 |
| Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective |
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0 |
| Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity |
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✅ |
✅ |
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4 |
| DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models |
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✅ |
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❌ |
❌ |
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1 |
| Dropout Regularization Versus l2-Penalization in the Linear Model |
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❌ |
❌ |
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❌ |
❌ |
❌ |
0 |
| ENNS: Variable Selection, Regression, Classification, and Deep Neural Network for High-Dimensional Data |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Effect-Invariant Mechanisms for Policy Generalization |
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✅ |
✅ |
✅ |
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5 |
| Efficient Active Manifold Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization |
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✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| Efficient Convex Algorithms for Universal Kernel Learning |
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❌ |
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6 |
| Efficient Modality Selection in Multimodal Learning |
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✅ |
✅ |
❌ |
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4 |
| Empirical Design in Reinforcement Learning |
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❌ |
✅ |
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❌ |
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2 |
| Entropic Gromov-Wasserstein Distances: Stability and Algorithms |
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✅ |
✅ |
❌ |
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5 |
| Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design |
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❌ |
❌ |
❌ |
1 |
| Estimating the Replication Probability of Significant Classification Benchmark Experiments |
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✅ |
✅ |
✅ |
7 |
| Estimation of Sparse Gaussian Graphical Models with Hidden Clustering Structure |
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✅ |
❌ |
✅ |
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5 |
| Estimation of the Order of Non-Parametric Hidden Markov Models using the Singular Values of an Integral Operator |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Euler Characteristic Tools for Topological Data Analysis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Exploration of the Search Space of Gaussian Graphical Models for Paired Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fairness guarantees in multi-class classification with demographic parity |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fairness in Survival Analysis with Distributionally Robust Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| False discovery proportion envelopes with m-consistency |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Rates in Pool-Based Batch Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Faster Randomized Methods for Orthogonality Constrained Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Faster Rates of Differentially Private Stochastic Convex Optimization |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fat-Shattering Dimension of k-fold Aggregations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Federated Automatic Differentiation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| FineMorphs: Affine-Diffeomorphic Sequences for Regression |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fisher information dissipation for time-inhomogeneous stochastic differential equations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Fixed points of nonnegative neural networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Flexible Bayesian Product Mixture Models for Vector Autoregressions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Fortuna: A Library for Uncertainty Quantification in Deep Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
4 |
| From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fréchet Random Forests for Metric Space Valued Regression with Non Euclidean Predictors |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Functional Directed Acyclic Graphs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Functional optimal transport: regularized map estimation and domain adaptation for functional data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Functions with average smoothness: structure, algorithms, and learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| GGD: Grafting Gradient Descent |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Gaussian Interpolation Flows |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Gaussian Mixture Models with Rare Events |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generalization and Stability of Interpolating Neural Networks with Minimal Width |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization on the Unseen, Logic Reasoning and Degree Curriculum |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generative Adversarial Ranking Nets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Geometric Learning with Positively Decomposable Kernels |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Goal-Space Planning with Subgoal Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Gradual Domain Adaptation: Theory and Algorithms |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Grokking phase transitions in learning local rules with gradient descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random steps |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Heterogeneous-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Constrained Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| How Two-Layer Neural Networks Learn, One (Giant) Step at a Time |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Random Features for Dot Product Kernels |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving physics-informed neural networks with meta-learned optimization |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Individual-centered Partial Information in Social Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Inference on High-dimensional Single-index Models with Streaming Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Infinite-Dimensional Diffusion Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Information Capacity Regret Bounds for Bandits with Mediator Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Information Processing Equalities and the Information–Risk Bridge |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Information-Theoretic Generalization Bounds for Transductive Learning and its Applications |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Interpretable algorithmic fairness in structured and unstructured data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Invariant Physics-Informed Neural Networks for Ordinary Differential Equations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Invariant and Equivariant Reynolds Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Iterate Averaging in the Quest for Best Test Error |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| KerasCV and KerasNLP: Multi-framework Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Kernel Thinning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Label Alignment Regularization for Distribution Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Label Noise Robustness of Conformal Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learnability of Linear Port-Hamiltonian Systems |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Discretized Neural Networks under Ricci Flow |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Gaussian DAGs from Network Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Regularized Graphon Mean-Field Games with Unknown Graphons |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning and scoring Gaussian latent variable causal models with unknown additive interventions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning from many trajectories |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning to Warm-Start Fixed-Point Optimization Algorithms |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning with a linear loss function: excess risk and estimation bounds for ERM, minmax MOM and their regularized versions with applications to robustness in sparse PCA. |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Linear Distance Metric Learning with Noisy Labels |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Linear Regression With Unmatched Data: A Deconvolution Perspective |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Localisation of Regularised and Multiview Support Vector Machine Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Logistic Regression Under Network Dependence |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Low-Rank Matrix Estimation in the Presence of Change-Points |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Low-rank Variational Bayes correction to the Laplace method |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Lower Bounds on the Bayesian Risk via Information Measures |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Lower Complexity Adaptation for Empirical Entropic Optimal Transport |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| MAP- and MLE-Based Teaching |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| MLRegTest: A Benchmark for the Machine Learning of Regular Languages |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Manifold Learning by Mixture Models of VAEs for Inverse Problems |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Margin-Based Active Learning of Classifiers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Materials Discovery using Max K-Armed Bandit |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mathematical Framework for Online Social Media Auditing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL) |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Mean-Field Games With Finitely Many Players: Independent Learning and Subjectivity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Memorization With Neural Nets: Going Beyond the Worst Case |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Memory of recurrent networks: Do we compute it right? |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Memory-Efficient Sequential Pattern Mining with Hybrid Tries |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mentored Learning: Improving Generalization and Convergence of Student Learner |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Minimax Rates for High-Dimensional Random Tessellation Forests |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Model-Free Representation Learning and Exploration in Low-Rank MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Modeling Random Networks with Heterogeneous Reciprocity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multi-Objective Neural Architecture Search by Learning Search Space Partitions |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Response Linear Discriminant Analysis in High Dimensions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multiple Descent in the Multiple Random Feature Model |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nearest Neighbor Sampling for Covariate Shift Adaptation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Bayes estimators for censored inference with peaks-over-threshold models |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Feature Learning in Function Space |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-Euclidean Monotone Operator Theory and Applications |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-splitting Neyman-Pearson Classifiers |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nonparametric Copula Models for Multivariate, Mixed, and Missing Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nonparametric Inference under B-bits Quantization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nonparametric Regression for 3D Point Cloud Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| On Regularized Radon-Nikodym Differentiation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Sufficient Graphical Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Tail Decay Rate Estimation of Loss Function Distributions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Truthing Issues in Supervised Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Unbiased Estimation for Partially Observed Diffusions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Concentration of the Minimizers of Empirical Risks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Connection between Lp- and Risk Consistency and its Implications on Regularized Kernel Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Generalization of Stochastic Gradient Descent with Momentum |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Hyperparameters in Stochastic Gradient Descent with Momentum |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Intrinsic Structures of Spiking Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Learnability of Out-of-distribution Detection |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternatives |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Optimality of Misspecified Spectral Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Open-Source Conversational AI with SpeechBrain 1.0 |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| OpenBox: A Python Toolkit for Generalized Black-box Optimization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Operator learning without the adjoint |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Clustering with Bandit Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Optimal First-Order Algorithms as a Function of Inequalities |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Optimal Learning Policies for Differential Privacy in Multi-armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Locally Private Nonparametric Classification with Public Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Weighted Random Forests |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Optimization-based Causal Estimation from Heterogeneous Environments |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Overparametrized Multi-layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PAMI: An Open-Source Python Library for Pattern Mining |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Parallel-in-Time Probabilistic Numerical ODE Solvers |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Pareto Smoothed Importance Sampling |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Pearl: A Production-Ready Reinforcement Learning Agent |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Personalized PCA: Decoupling Shared and Unique Features |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Policy Gradient Methods in the Presence of Symmetries and State Abstractions |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Post-Regularization Confidence Bands for Ordinary Differential Equations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Pre-trained Gaussian Processes for Bayesian Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Predictive Inference with Weak Supervision |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PromptBench: A Unified Library for Evaluation of Large Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Pure Differential Privacy for Functional Summaries with a Laplace-like Process |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| PyDMD: A Python Package for Robust Dynamic Mode Decomposition |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PyGOD: A Python Library for Graph Outlier Detection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pygmtools: A Python Graph Matching Toolkit |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Random Forest Weighted Local Fréchet Regression with Random Objects |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Random Smoothing Regularization in Kernel Gradient Descent Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Random Subgraph Detection Using Queries |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Random measure priors in Bayesian recovery from sketches |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rates of convergence for density estimation with generative adversarial networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Recursive Estimation of Conditional Kernel Mean Embeddings |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regimes of No Gain in Multi-class Active Learning |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Regret Analysis of Bilateral Trade with a Smoothed Adversary |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Representation Learning via Manifold Flattening and Reconstruction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Resource-Efficient Neural Networks for Embedded Systems |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Revisiting RIP Guarantees for Sketching Operators on Mixture Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Risk Measures and Upper Probabilities: Coherence and Stratification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Principal Component Analysis using Density Power Divergence |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Spectral Clustering with Rank Statistics |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample-efficient Adversarial Imitation Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable Resampling in Massive Generalized Linear Models via Subsampled Residual Bootstrap |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Scaling Instruction-Finetuned Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scaling Speech Technology to 1,000+ Languages |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scaling the Convex Barrier with Sparse Dual Algorithms |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Semi-supervised Inference for Block-wise Missing Data without Imputation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sharp analysis of power iteration for tensor PCA |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sharpness-Aware Minimization and the Edge of Stability |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Simple Cycle Reservoirs are Universal |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sparse Graphical Linear Dynamical Systems |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Sparse NMF with Archetypal Regularization: Computational and Robustness Properties |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sparse Recovery With Multiple Data Streams: An Adaptive Sequential Testing Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Spatial meshing for general Bayesian multivariate models |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Spectral Regularized Kernel Goodness-of-Fit Tests |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Spectral learning of multivariate extremes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spherical Rotation Dimension Reduction with Geometric Loss Functions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Split Conformal Prediction and Non-Exchangeable Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stability and L2-penalty in Model Averaging |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stable Implementation of Probabilistic ODE Solvers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stable and Consistent Density-Based Clustering via Multiparameter Persistence |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Stage-Aware Learning for Dynamic Treatments |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Statistical Inference for Fairness Auditing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Statistical analysis for a penalized EM algorithm in high-dimensional mixture linear regression model |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stochastic Regularized Majorization-Minimization with weakly convex and multi-convex surrogates |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic-Constrained Stochastic Optimization with Markovian Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Structured Optimal Variational Inference for Dynamic Latent Space Models |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Studying the Interplay between Information Loss and Operation Loss in Representations for Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sum-of-norms clustering does not separate nearby balls |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Tangential Wasserstein Projections |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Targeted Separation and Convergence with Kernel Discrepancies |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Tensor-train methods for sequential state and parameter learning in state-space models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Non-Overlapping Statistical Approximation to Overlapping Group Lasso |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| The Nyström method for convex loss functions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| TopoX: A Suite of Python Packages for Machine Learning on Topological Domains |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Topological Analysis for Detecting Anomalies in dependent sequences: application to Time Series |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Topological Node2vec: Enhanced Graph Embedding via Persistent Homology |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Explainable Evaluation Metrics for Machine Translation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards Unbiased Exploration in Partial Label Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Trained Transformers Learn Linear Models In-Context |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST) |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Transfer learning for tensor Gaussian graphical models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Transport-based Counterfactual Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Two is Better Than One: Regularized Shrinkage of Large Minimum Variance Portfolios |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Uncertainty Quantification of MLE for Entity Ranking with Covariates |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding Entropic Regularization in GANs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unified Binary and Multiclass Margin-Based Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Unlabeled Principal Component Analysis and Matrix Completion |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Tree Boosting for Learning Probability Distributions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Value-Distributional Model-Based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Variance estimation in graphs with the fused lasso |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Virtual-Event-Based Posterior Sampling and Inference for Neyman-Scott Processes |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Volterra Neural Networks (VNNs) |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Wasserstein Proximal Coordinate Gradient Algorithms |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Zeroth-order Stochastic Approximation Algorithms for DR-submodular Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| aeon: a Python Toolkit for Learning from Time Series |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| pgmpy: A Python Toolkit for Bayesian Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| ptwt - The PyTorch Wavelet Toolbox |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| skscope: Fast Sparsity-Constrained Optimization in Python |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |