| A Bayesian Bradley-Terry model to compare multiple ML algorithms on multiple data sets |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| A Complete Characterization of Linear Estimators for Offline Policy Evaluation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Continuous-time Stochastic Gradient Descent Method for Continuous Data |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| A First Look into the Carbon Footprint of Federated Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| A Framework and Benchmark for Deep Batch Active Learning for Regression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| A Novel Integer Linear Programming Approach for Global L0 Minimization |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| A PDE approach for regret bounds under partial monitoring |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| A Permutation-Free Kernel Independence Test |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Randomized Subspace-based Approach for Dimensionality Reduction and Important Variable Selection |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Relaxed Inertial Forward-Backward-Forward Algorithm for Solving Monotone Inclusions with Application to GANs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Rigorous Information-Theoretic Definition of Redundancy and Relevancy in Feature Selection Based on (Partial) Information Decomposition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Unified Analysis of Multi-task Functional Linear Regression Models with Manifold Constraint and Composite Quadratic Penalty |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Unified Approach to Controlling Implicit Regularization via Mirror Descent |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Unified Framework for Optimization-Based Graph Coarsening |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Unified Theory of Diversity in Ensemble Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Accelerated Primal-Dual Mirror Dynamics for Centralized and Distributed Constrained Convex Optimization Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Adaptation Augmented Model-based Policy Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptation to the Range in K-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Adaptive Clustering Using Kernel Density Estimators |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Data Depth via Multi-Armed Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adaptive False Discovery Rate Control with Privacy Guarantee |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adaptive Learning of Density Ratios in RKHS |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| An Analysis of Robustness of Non-Lipschitz Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| An Eigenmodel for Dynamic Multilayer Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| An Empirical Investigation of the Role of Pre-training in Lifelong Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Approximate Post-Selective Inference for Regression with the Group LASSO |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Asymptotics of Network Embeddings Learned via Subsampling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Atlas: Few-shot Learning with Retrieval Augmented Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Attacks against Federated Learning Defense Systems and their Mitigation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Attribution-based Explanations that Provide Recourse Cannot be Robust |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Augmented Sparsifiers for Generalized Hypergraph Cuts |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| AutoKeras: An AutoML Library for Deep Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Autoregressive Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Avalanche: A PyTorch Library for Deep Continual Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Bagging in overparameterized learning: Risk characterization and risk monotonization |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bandit problems with fidelity rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Data Selection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Bayesian Spiked Laplacian Graphs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Benchmarking Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Benign Overfitting of Constant-Stepsize SGD for Linear Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Benign overfitting in ridge regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Beyond Spectral Gap: The Role of the Topology in Decentralized Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond the Golden Ratio for Variational Inequality Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Boosting Multi-agent Reinforcement Learning via Contextual Prompting |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Buffered Asynchronous SGD for Byzantine Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Calibrated Multiple-Output Quantile Regression with Representation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Causal Bandits for Linear Structural Equation Models |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Causal Discovery with Unobserved Confounding and Non-Gaussian Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Cluster-Specific Predictions with Multi-Task Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Clustering and Structural Robustness in Causal Diagrams |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
5 |
| Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Combinatorial Optimization and Reasoning with Graph Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Community Recovery in the Geometric Block Model |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Community models for networks observed through edge nominations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Comprehensive Algorithm Portfolio Evaluation using Item Response Theory |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Compression, Generalization and Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Concentration analysis of multivariate elliptic diffusions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Confidence Intervals and Hypothesis Testing for High-dimensional Quantile Regression: Convolution Smoothing and Debiasing |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Confidence and Uncertainty Assessment for Distributional Random Forests |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Contextual Stochastic Block Model: Sharp Thresholds and Contiguity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Continuous-in-time Limit for Bayesian Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convex Reinforcement Learning in Finite Trials |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| DART: Distance Assisted Recursive Testing |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Decentralized Learning: Theoretical Optimality and Practical Improvements |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Decentralized Robust V-learning for Solving Markov Games with Model Uncertainty |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep linear networks can benignly overfit when shallow ones do |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deletion and Insertion Tests in Regression Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Density estimation on low-dimensional manifolds: an inflation-deflation approach |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Differentially Private Hypothesis Testing for Linear Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dimension Reduction and MARS |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dimensionality Reduction and Wasserstein Stability for Kernel Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Dimensionless machine learning: Imposing exact units equivariance |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Discovering Salient Neurons in deep NLP models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Discrete Variational Calculus for Accelerated Optimization |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distributed Algorithms for U-statistics-based Empirical Risk Minimization |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Distributed Community Detection in Large Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Distributed Sparse Regression via Penalization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Distributed Statistical Inference under Heterogeneity |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Divide-and-Conquer Fusion |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dropout Training is Distributionally Robust Optimal |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Dynamic Ranking with the BTL Model: A Nearest Neighbor based Rank Centrality Method |
✅ |
✅ |
✅ |
❌ |
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4 |
| Efficient Computation of Rankings from Pairwise Comparisons |
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✅ |
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❌ |
1 |
| Efficient Structure-preserving Support Tensor Train Machine |
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✅ |
7 |
| Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net |
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❌ |
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6 |
| Entropic Fictitious Play for Mean Field Optimization Problem |
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4 |
| Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm |
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0 |
| Escaping The Curse of Dimensionality in Bayesian Model-Based Clustering |
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❌ |
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❌ |
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5 |
| Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model |
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❌ |
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❌ |
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4 |
| Euler-Lagrange Analysis of Generative Adversarial Networks |
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❌ |
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✅ |
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5 |
| Evaluating Instrument Validity using the Principle of Independent Mechanisms |
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2 |
| Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators |
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✅ |
❌ |
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❌ |
✅ |
4 |
| Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions |
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✅ |
❌ |
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✅ |
5 |
| F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning |
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❌ |
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4 |
| FLIP: A Utility Preserving Privacy Mechanism for Time Series |
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✅ |
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2 |
| Factor Graph Neural Networks |
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❌ |
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4 |
| Fair Data Representation for Machine Learning at the Pareto Frontier |
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❌ |
✅ |
6 |
| Fairlearn: Assessing and Improving Fairness of AI Systems |
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❌ |
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❌ |
❌ |
2 |
| Faith-Shap: The Faithful Shapley Interaction Index |
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✅ |
❌ |
❌ |
✅ |
4 |
| Fast Expectation Propagation for Heteroscedastic, Lasso-Penalized, and Quantile Regression |
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✅ |
✅ |
✅ |
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❌ |
✅ |
6 |
| Fast Objective & Duality Gap Convergence for Non-Convex Strongly-Concave Min-Max Problems with PL Condition |
✅ |
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❌ |
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✅ |
4 |
| Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics |
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❌ |
❌ |
✅ |
5 |
| Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation |
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❌ |
✅ |
✅ |
✅ |
6 |
| FedLab: A Flexible Federated Learning Framework |
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❌ |
❌ |
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❌ |
❌ |
2 |
| Finding Groups of Cross-Correlated Features in Bi-View Data |
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✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems |
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✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation |
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✅ |
✅ |
✅ |
7 |
| Flexible Model Aggregation for Quantile Regression |
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✅ |
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✅ |
❌ |
❌ |
✅ |
4 |
| Foundation Models and Fair Use |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fourier Neural Operator with Learned Deformations for PDEs on General Geometries |
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✅ |
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✅ |
✅ |
❌ |
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4 |
| From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Functional L-Optimality Subsampling for Functional Generalized Linear Models with Massive Data |
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✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Fundamental limits and algorithms for sparse linear regression with sublinear sparsity |
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❌ |
❌ |
✅ |
2 |
| GANs as Gradient Flows that Converge |
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❌ |
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❌ |
❌ |
❌ |
❌ |
1 |
| GFlowNet Foundations |
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❌ |
❌ |
❌ |
❌ |
0 |
| Gap Minimization for Knowledge Sharing and Transfer |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Gaussian Processes with Errors in Variables: Theory and Computation |
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✅ |
❌ |
✅ |
4 |
| Generalization Bounds for Adversarial Contrastive Learning |
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❌ |
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✅ |
4 |
| Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels |
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✅ |
4 |
| Generalization error bounds for multiclass sparse linear classifiers |
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4 |
| Generalized Linear Models in Non-interactive Local Differential Privacy with Public Data |
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4 |
| Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables |
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❌ |
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❌ |
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5 |
| Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization |
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❌ |
❌ |
❌ |
✅ |
2 |
| Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation |
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✅ |
✅ |
❌ |
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6 |
| Graph Attention Retrospective |
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✅ |
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❌ |
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✅ |
3 |
| Graph Clustering with Graph Neural Networks |
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❌ |
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4 |
| Graph-Aided Online Multi-Kernel Learning |
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❌ |
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❌ |
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5 |
| Group SLOPE Penalized Low-Rank Tensor Regression |
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6 |
| Hard-Constrained Deep Learning for Climate Downscaling |
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❌ |
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5 |
| HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn |
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❌ |
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5 |
| HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation |
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✅ |
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❌ |
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5 |
| Hierarchical Kernels in Deep Kernel Learning |
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4 |
| High-Dimensional Inference for Generalized Linear Models with Hidden Confounding |
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✅ |
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❌ |
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2 |
| Higher-Order Spectral Clustering Under Superimposed Stochastic Block Models |
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3 |
| How Do You Want Your Greedy: Simultaneous or Repeated? |
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❌ |
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❌ |
✅ |
4 |
| Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
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✅ |
2 |
| Implicit Regularization and Entrywise Convergence of Riemannian Optimization for Low Tucker-Rank Tensor Completion |
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❌ |
❌ |
❌ |
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2 |
| Importance Sparsification for Sinkhorn Algorithm |
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❌ |
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❌ |
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5 |
| Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning |
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✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Improving multiple-try Metropolis with local balancing |
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✅ |
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❌ |
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❌ |
✅ |
4 |
| Incremental Learning in Diagonal Linear Networks |
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❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Inference for a Large Directed Acyclic Graph with Unspecified Interventions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Inference on the Change Point under a High Dimensional Covariance Shift |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations |
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❌ |
❌ |
❌ |
✅ |
2 |
| Insights into Ordinal Embedding Algorithms: A Systematic Evaluation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Instance-Dependent Confidence and Early Stopping for Reinforcement Learning |
✅ |
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❌ |
✅ |
❌ |
❌ |
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3 |
| Instance-Dependent Generalization Bounds via Optimal Transport |
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❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Integrating Random Effects in Deep Neural Networks |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Interpolating Classifiers Make Few Mistakes |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Interpretable and Fair Boolean Rule Sets via Column Generation |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Intrinsic Persistent Homology via Density-based Metric Learning |
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✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality |
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✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| Jump Interval-Learning for Individualized Decision Making with Continuous Treatments |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition |
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✅ |
❌ |
✅ |
❌ |
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5 |
| Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches |
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✅ |
❌ |
❌ |
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3 |
| Knowledge Hypergraph Embedding Meets Relational Algebra |
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❌ |
✅ |
6 |
| L0Learn: A Scalable Package for Sparse Learning using L0 Regularization |
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✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Label Distribution Changing Learning with Sample Space Expanding |
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✅ |
❌ |
❌ |
✅ |
5 |
| Labels, Information, and Computation: Efficient Learning Using Sufficient Labels |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LapGym - An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large data limit of the MBO scheme for data clustering: convergence of the dynamics |
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❌ |
❌ |
❌ |
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1 |
| Large sample spectral analysis of graph-based multi-manifold clustering |
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✅ |
❌ |
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❌ |
✅ |
4 |
| Leaky Hockey Stick Loss: The First Negatively Divergent Margin-based Loss Function for Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Learning Conditional Generative Models for Phase Retrieval |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition |
✅ |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Mean-Field Games with Discounted and Average Costs |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Optimal Feedback Operators and their Sparse Polynomial Approximations |
✅ |
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❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments |
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✅ |
✅ |
❌ |
✅ |
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5 |
| Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces |
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❌ |
❌ |
✅ |
3 |
| Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Rank under Multinomial Logit Choice |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning-augmented count-min sketches via Bayesian nonparametrics |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Least Squares Model Averaging for Distributed Data |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| LibMTL: A Python Library for Deep Multi-Task Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Lifted Bregman Training of Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Limitations on approximation by deep and shallow neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Limits of Dense Simplicial Complexes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Low Tree-Rank Bayesian Vector Autoregression Models |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence |
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✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Lower Bounds and Accelerated Algorithms for Bilevel Optimization |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| MARS: A Second-Order Reduction Algorithm for High-Dimensional Sparse Precision Matrices Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| MAUVE Scores for Generative Models: Theory and Practice |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MMD Aggregated Two-Sample Test |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Maximum likelihood estimation in Gaussian process regression is ill-posed |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Merlion: End-to-End Machine Learning for Time Series |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Metrizing Weak Convergence with Maximum Mean Discrepancies |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Microcanonical Hamiltonian Monte Carlo |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mini-batching error and adaptive Langevin dynamics |
❌ |
❌ |
✅ |
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❌ |
❌ |
✅ |
2 |
| Minimal Width for Universal Property of Deep RNN |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minimax Risk Classifiers with 0-1 Loss |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Mixed Regression via Approximate Message Passing |
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❌ |
❌ |
❌ |
✅ |
2 |
| Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Model-based Causal Discovery for Zero-Inflated Count Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Modular Regression: Improving Linear Models by Incorporating Auxiliary Data |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Monotonic Alpha-divergence Minimisation for Variational Inference |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Consensus Decentralized Accelerated Gradient Descent |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-view Collaborative Gaussian Process Dynamical Systems |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multilevel CNNs for Parametric PDEs |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multiplayer Performative Prediction: Learning in Decision-Dependent Games |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Near-Optimal Weighted Matrix Completion |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Nearest Neighbor Dirichlet Mixtures |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Q-learning for solving PDEs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Non-stationary Online Learning with Memory and Non-stochastic Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Off-Policy Actor-Critic with Emphatic Weightings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Batch Teaching Without Collusion |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Biased Compression for Distributed Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Distance and Kernel Measures of Conditional Dependence |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Learning Rates and Schrödinger Operators |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Tilted Losses in Machine Learning: Theory and Applications |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| On the Dynamics Under the Unhinged Loss and Beyond |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the geometry of Stein variational gradient descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Change-Point Detection in High-Dimensional Covariance Structure with Application to Dynamic Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Online Non-stochastic Control with Partial Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Optimization over Riemannian Manifolds |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Operator learning with PCA-Net: upper and lower complexity bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Convergence Rates for Distributed Nystroem Approximation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Optimal Parameter-Transfer Learning by Semiparametric Model Averaging |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Optimal Strategies for Reject Option Classifiers |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Outlier-Robust Subsampling Techniques for Persistent Homology |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Over-parameterized Deep Nonparametric Regression for Dependent Data with Its Applications to Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| PAC-learning for Strategic Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| PaLM: Scaling Language Modeling with Pathways |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pivotal Estimation of Linear Discriminant Analysis in High Dimensions |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Posterior Consistency for Bayesian Relevance Vector Machines |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Posterior Contraction for Deep Gaussian Process Priors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Prediction Equilibrium for Dynamic Network Flows |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Principled Out-of-Distribution Detection via Multiple Testing |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Privacy-Aware Rejection Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AI |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ProtoryNet - Interpretable Text Classification Via Prototype Trajectories |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Provably Sample-Efficient Model-Free Algorithm for MDPs with Peak Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Python package for causal discovery based on LiNGAM |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantifying Network Similarity using Graph Cumulants |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Quasi-Equivalence between Width and Depth of Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| RVCL: Evaluating the Robustness of Contrastive Learning via Verification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Radial Basis Approximation of Tensor Fields on Manifolds: From Operator Estimation to Manifold Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Random Feature Amplification: Feature Learning and Generalization in Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Random Feature Neural Networks Learn Black-Scholes Type PDEs Without Curse of Dimensionality |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Random Forests for Change Point Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Randomized Spectral Co-Clustering for Large-Scale Directed Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| RankSEG: A Consistent Ranking-based Framework for Segmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Recursive Quantile Estimation: Non-Asymptotic Confidence Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regularized Joint Mixture Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reinforcement Learning for Joint Optimization of Multiple Rewards |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Removing Data Heterogeneity Influence Enhances Network Topology Dependence of Decentralized SGD |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reproducing Kernels and New Approaches in Compositional Data Analysis |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
1 |
| Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the in the O(epsilon^(-7/4)) Complexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Revisiting inference after prediction |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting minimum description length complexity in overparameterized models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Ridges, Neural Networks, and the Radon Transform |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Risk Bounds for Positive-Unlabeled Learning Under the Selected At Random Assumption |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Robust High-Dimensional Low-Rank Matrix Estimation: Optimal Rate and Data-Adaptive Tuning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Load Balancing with Machine Learned Advice |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Methods for High-Dimensional Linear Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sample Complexity for Distributionally Robust Learning under chi-square divergence |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sampling random graph homomorphisms and applications to network data analysis |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scalable Computation of Causal Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scale Invariant Power Iteration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scaling Up Models and Data with t5x and seqio |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Selection by Prediction with Conformal p-values |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Selective inference for k-means clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Semiparametric Inference Using Fractional Posteriors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sensing Theorems for Unsupervised Learning in Linear Inverse Problems |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sensitivity-Free Gradient Descent Algorithms |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Set-valued Classification with Out-of-distribution Detection for Many Classes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Single Timescale Actor-Critic Method to Solve the Linear Quadratic Regulator with Convergence Guarantees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Small Transformers Compute Universal Metric Embeddings |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sparse GCA and Thresholded Gradient Descent |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sparse Graph Learning from Spatiotemporal Time Series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sparse Markov Models for High-dimensional Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sparse PCA: a Geometric Approach |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
6 |
| Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Statistical Comparisons of Classifiers by Generalized Stochastic Dominance |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Statistical Inference for Noisy Incomplete Binary Matrix |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Statistical Robustness of Empirical Risks in Machine Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stochastic Optimization under Distributional Drift |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Strategic Knowledge Transfer |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| T-Cal: An Optimal Test for the Calibration of Predictive Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Temporal Abstraction in Reinforcement Learning with the Successor Representation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Art of BART: Minimax Optimality over Nonhomogeneous Smoothness in High Dimension |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Bayesian Learning Rule |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Brier Score under Administrative Censoring: Problems and a Solution |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Dynamics of Sharpness-Aware Minimization: Bouncing Across Ravines and Drifting Towards Wide Minima |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Geometry and Calculus of Losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Hyperspherical Geometry of Community Detection: Modularity as a Distance |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Implicit Bias of Benign Overfitting |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Measure and Mismeasure of Fairness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Power of Contrast for Feature Learning: A Theoretical Analysis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| The Proximal ID Algorithm |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The d-Separation Criterion in Categorical Probability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The multimarginal optimal transport formulation of adversarial multiclass classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Topological Convolutional Layers for Deep Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Topological Hidden Markov Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| TorchOpt: An Efficient Library for Differentiable Optimization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Towards Learning to Imitate from a Single Video Demonstration |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Tractable and Near-Optimal Adversarial Algorithms for Robust Estimation in Contaminated Gaussian Models |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tree-AMP: Compositional Inference with Tree Approximate Message Passing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Two Sample Testing in High Dimension via Maximum Mean Discrepancy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Unbiased Multilevel Monte Carlo Methods for Intractable Distributions: MLMC Meets MCMC |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Universal Approximation Property of Invertible Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Variational Inference for Deblending Crowded Starfields |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-Varying Covariates |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Weisfeiler and Leman go Machine Learning: The Story so far |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| When Locally Linear Embedding Hits Boundary |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Zeroth-Order Alternating Gradient Descent Ascent Algorithms for A Class of Nonconvex-Nonconcave Minimax Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| q-Learning in Continuous Time |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| skrl: Modular and Flexible Library for Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |