| "What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts |
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4 |
| (De)-regularized Maximum Mean Discrepancy Gradient Flow |
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4 |
| A Comparative Evaluation of Quantification Methods |
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4 |
| A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds |
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4 |
| A Hybrid Weighted Nearest Neighbour Classifier for Semi-Supervised Learning |
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3 |
| A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization |
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4 |
| A Random Matrix Approach to Low-Multilinear-Rank Tensor Approximation |
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2 |
| A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs |
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3 |
| A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning |
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4 |
| Accelerating optimization over the space of probability measures |
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2 |
| Actor-Critic learning for mean-field control in continuous time |
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2 |
| Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback |
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5 |
| Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos |
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6 |
| Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization |
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4 |
| Affine Rank Minimization via Asymptotic Log-Det Iteratively Reweighted Least Squares |
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3 |
| Algorithms for ridge estimation with convergence guarantees |
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3 |
| An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition |
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4 |
| An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models |
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6 |
| An Augmentation Overlap Theory of Contrastive Learning |
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4 |
| An Axiomatic Definition of Hierarchical Clustering |
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0 |
| Are Ensembles Getting Better All the Time? |
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4 |
| Assumption-lean and data-adaptive post-prediction inference |
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5 |
| Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection |
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5 |
| Autoencoders in Function Space |
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5 |
| Bagged Regularized k-Distances for Anomaly Detection |
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3 |
| Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets |
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4 |
| Bayes Meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes |
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0 |
| Bayesian Data Sketching for Varying Coefficient Regression Models |
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6 |
| Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data |
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5 |
| Bayesian Scalar-on-Image Regression with a Spatially Varying Single-layer Neural Network Prior |
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4 |
| Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions |
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5 |
| Best Linear Unbiased Estimate from Privatized Contingency Tables |
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5 |
| Biological Sequence Kernels with Guaranteed Flexibility |
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4 |
| BitNet: 1-bit Pre-training for Large Language Models |
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4 |
| BoFire: Bayesian Optimization Framework Intended for Real Experiments |
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1 |
| Boosting Causal Additive Models |
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5 |
| Calibrated Inference: Statistical Inference that Accounts for Both Sampling Uncertainty and Distributional Uncertainty |
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5 |
| Categorical Semantics of Compositional Reinforcement Learning |
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0 |
| Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability |
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2 |
| Causal Effect of Functional Treatment |
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5 |
| Characterizing Dynamical Stability of Stochastic Gradient Descent in Overparameterized Learning |
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0 |
| Classification in the high dimensional Anisotropic mixture framework: A new take on Robust Interpolation |
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1 |
| ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation |
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6 |
| Collaborative likelihood-ratio estimation over graphs |
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5 |
| Composite Goodness-of-fit Tests with Kernels |
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5 |
| Conditional Wasserstein Distances with Applications in Bayesian OT Flow Matching |
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4 |
| Contextual Bandits with Stage-wise Constraints |
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2 |
| Continuously evolving rewards in an open-ended environment |
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2 |
| Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation |
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3 |
| Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs |
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3 |
| Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding |
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3 |
| Curvature-based Clustering on Graphs |
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3 |
| DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning |
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5 |
| DRM Revisited: A Complete Error Analysis |
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1 |
| Data-Driven Performance Guarantees for Classical and Learned Optimizers |
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5 |
| Decentralized Asynchronous Optimization with DADAO allows Decoupling and Acceleration |
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3 |
| Decentralized Bilevel Optimization: A Perspective from Transient Iteration Complexity |
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4 |
| Decentralized Sparse Linear Regression via Gradient-Tracking |
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3 |
| Deep Generative Models: Complexity, Dimensionality, and Approximation |
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3 |
| Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation |
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2 |
| Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization |
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5 |
| Deep Variational Multivariate Information Bottleneck - A Framework for Variational Losses |
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3 |
| Degree of Interference: A General Framework For Causal Inference Under Interference |
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4 |
| Deletion Robust Non-Monotone Submodular Maximization over Matroids |
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1 |
| Density Estimation Using the Perceptron |
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0 |
| Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems |
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4 |
| Determine the Number of States in Hidden Markov Models via Marginal Likelihood |
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4 |
| Diffeomorphism-based feature learning using Poincaré inequalities on augmented input space |
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4 |
| Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies |
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4 |
| Differentially Private Multivariate Medians |
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3 |
| Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables |
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5 |
| DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data |
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2 |
| Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis |
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3 |
| Distribution Estimation under the Infinity Norm |
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2 |
| Distribution Free Tests for Model Selection Based on Maximum Mean Discrepancy with Estimated Parameters |
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4 |
| Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models |
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6 |
| Dynamic angular synchronization under smoothness constraints |
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3 |
| EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback |
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6 |
| EMaP: Explainable AI with Manifold-based Perturbations |
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5 |
| Early Alignment in Two-Layer Networks Training is a Two-Edged Sword |
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2 |
| Efficient Knowledge Deletion from Trained Models Through Layer-wise Partial Machine Unlearning |
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6 |
| Efficient Methods for Non-stationary Online Learning |
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3 |
| Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling |
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5 |
| Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition |
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6 |
| Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response |
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3 |
| Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data |
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6 |
| Efficiently Escaping Saddle Points in Bilevel Optimization |
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2 |
| Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods |
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5 |
| Enhancing Graph Representation Learning with Localized Topological Features |
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6 |
| Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities |
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1 |
| Error estimation and adaptive tuning for unregularized robust M-estimator |
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2 |
| Estimating Network-Mediated Causal Effects via Principal Components Network Regression |
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3 |
| Estimation of Local Geometric Structure on Manifolds from Noisy Data |
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3 |
| Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings |
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3 |
| Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data |
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4 |
| Extending Temperature Scaling with Homogenizing Maps |
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3 |
| Extremal graphical modeling with latent variables via convex optimization |
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4 |
| Fair Text Classification via Transferable Representations |
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5 |
| Fast Algorithm for Constrained Linear Inverse Problems |
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5 |
| Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction |
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6 |
| Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers |
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0 |
| Fine-Grained Change Point Detection for Topic Modeling with Pitman-Yor Process |
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3 |
| Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems |
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1 |
| Finite Expression Method for Solving High-Dimensional Partial Differential Equations |
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3 |
| Four Axiomatic Characterizations of the Integrated Gradients Attribution Method |
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0 |
| Frequentist Guarantees of Distributed (Non)-Bayesian Inference |
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0 |
| From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective |
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1 |
| Frontiers to the learning of nonparametric hidden Markov models |
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1 |
| Fundamental Limits of Membership Inference Attacks on Machine Learning Models |
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3 |
| General Loss Functions Lead to (Approximate) Interpolation in High Dimensions |
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2 |
| Generalized multi-view model: Adaptive density estimation under low-rank constraints |
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4 |
| Generation of Geodesics with Actor-Critic Reinforcement Learning to Predict Midpoints |
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4 |
| Generative Adversarial Networks: Dynamics |
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0 |
| Geometry and Stability of Supervised Learning Problems |
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0 |
| Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2 |
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6 |
| Graph-accelerated Markov Chain Monte Carlo using Approximate Samples |
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4 |
| GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia |
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2 |
| Hierarchical Decision Making Based on Structural Information Principles |
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5 |
| Hierarchical and Stochastic Crystallization Learning: Geometrically Leveraged Nonparametric Regression with Delaunay Triangulation |
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4 |
| High-Dimensional L2-Boosting: Rate of Convergence |
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5 |
| High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces |
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5 |
| Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval |
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5 |
| How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences |
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3 |
| Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models |
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5 |
| Implicit vs Unfolded Graph Neural Networks |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Imprecise Multi-Armed Bandits: Representing Irreducible Uncertainty as a Zero-Sum Game |
✅ |
❌ |
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❌ |
❌ |
❌ |
1 |
| Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick |
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✅ |
❌ |
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5 |
| Inferring Change Points in High-Dimensional Regression via Approximate Message Passing |
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✅ |
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❌ |
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❌ |
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4 |
| Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection |
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❌ |
❌ |
✅ |
5 |
| Instability, Computational Efficiency and Statistical Accuracy |
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❌ |
❌ |
❌ |
❌ |
0 |
| Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Interpretable Global Minima of Deep ReLU Neural Networks on Sequentially Separable Data |
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❌ |
❌ |
❌ |
❌ |
0 |
| Invariant Subspace Decomposition |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Last-iterate Convergence of Shuffling Momentum Gradient Method under the Kurdyka-Lojasiewicz Inequality |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Latent Process Models for Functional Network Data |
✅ |
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✅ |
✅ |
❌ |
✅ |
6 |
| Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play |
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❌ |
✅ |
❌ |
✅ |
4 |
| Learning causal graphs via nonlinear sufficient dimension reduction |
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❌ |
✅ |
3 |
| Learning conditional distributions on continuous spaces |
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❌ |
✅ |
❌ |
✅ |
4 |
| Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning with Linear Function Approximations in Mean-Field Control |
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❌ |
✅ |
1 |
| Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation |
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✅ |
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✅ |
❌ |
❌ |
✅ |
5 |
| Lexicographic Lipschitz Bandits: New Algorithms and a Lower Bound |
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❌ |
❌ |
❌ |
✅ |
2 |
| Lightning UQ Box: Uncertainty Quantification for Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Linear Hypothesis Testing in High-Dimensional Expected Shortfall Regression with Heavy-Tailed Errors |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Linear Separation Capacity of Self-Supervised Representation Learning |
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✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals |
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✅ |
✅ |
✅ |
✅ |
5 |
| Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization |
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❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Locally Private Causal Inference for Randomized Experiments |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Losing Momentum in Continuous-time Stochastic Optimisation |
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❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Manifold Fitting under Unbounded Noise |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL |
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❌ |
❌ |
❌ |
✅ |
2 |
| Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Minimax Optimal Two-Sample Testing under Local Differential Privacy |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mixtures of Gaussian Process Experts with SMC^2 |
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❌ |
✅ |
❌ |
✅ |
4 |
| Model-free Change-Point Detection Using AUC of a Classifier |
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✅ |
✅ |
❌ |
✅ |
6 |
| Modelling Populations of Interaction Networks via Distance Metrics |
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❌ |
✅ |
❌ |
✅ |
4 |
| Multiple Instance Verification |
❌ |
✅ |
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✅ |
✅ |
✅ |
✅ |
6 |
| Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles |
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❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning |
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❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Consistent Bayesian Inference from Synthetic Data |
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✅ |
3 |
| On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control |
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✅ |
3 |
| On Inference for the Support Vector Machine |
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1 |
| On Model Identification and Out-of-Sample Prediction of PCR with Applications to Synthetic Controls |
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✅ |
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✅ |
❌ |
❌ |
✅ |
5 |
| On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Probabilistic Embeddings in Optimal Dimension Reduction |
❌ |
✅ |
❌ |
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❌ |
❌ |
1 |
| On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Approximation of Kernel functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Convergence of Projected Policy Gradient for Any Constant Step Sizes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Natural Gradient of the Evidence Lower Bound |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the O(sqrt(d)/T^(1/4)) Convergence Rate of RMSProp and Its Momentum Extension Measured by l_1 Norm |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference |
✅ |
❌ |
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❌ |
❌ |
❌ |
✅ |
2 |
| On the Robustness of Kernel Goodness-of-Fit Tests |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Quantile Regression |
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✅ |
✅ |
✅ |
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✅ |
4 |
| Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python |
❌ |
✅ |
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❌ |
❌ |
❌ |
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1 |
| Operator Learning for Hyperbolic PDEs |
✅ |
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❌ |
2 |
| Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal Experiment Design for Causal Effect Identification |
✅ |
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✅ |
❌ |
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❌ |
✅ |
4 |
| Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimal and Efficient Algorithms for Decentralized Online Convex Optimization |
✅ |
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❌ |
❌ |
❌ |
1 |
| Optimal subsampling for high-dimensional partially linear models via machine learning methods |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Optimization Over a Probability Simplex |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimizing Data Collection for Machine Learning |
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❌ |
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5 |
| Optimizing Return Distributions with Distributional Dynamic Programming |
❌ |
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✅ |
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❌ |
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3 |
| Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Outlier Robust and Sparse Estimation of Linear Regression Coefficients |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN |
❌ |
✅ |
❌ |
✅ |
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❌ |
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4 |
| Physics-informed Kernel Learning |
❌ |
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❌ |
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3 |
| Piecewise deterministic sampling with splitting schemes |
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3 |
| Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Posterior and Variational Inference for Deep Neural Networks with Heavy-Tailed Weights |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF |
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❌ |
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❌ |
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3 |
| Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms |
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4 |
| Quantifying the Effectiveness of Linear Preconditioning in Markov Chain Monte Carlo |
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❌ |
✅ |
2 |
| Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Random ReLU Neural Networks as Non-Gaussian Processes |
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❌ |
❌ |
❌ |
0 |
| Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees |
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❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Rank-one Convexification for Sparse Regression |
❌ |
❌ |
✅ |
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✅ |
✅ |
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5 |
| Recursive Causal Discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Regularized Rényi Divergence Minimization through Bregman Proximal Gradient Algorithms |
✅ |
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✅ |
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❌ |
✅ |
3 |
| Regularizing Hard Examples Improves Adversarial Robustness |
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✅ |
✅ |
✅ |
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6 |
| Reinforcement Learning for Infinite-Dimensional Systems |
✅ |
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❌ |
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❌ |
✅ |
3 |
| Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reliever: Relieving the Burden of Costly Model Fits for Changepoint Detection |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Riemannian Bilevel Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust Point Matching with Distance Profiles |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens |
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❌ |
❌ |
❌ |
✅ |
2 |
| Sampling and Estimation on Manifolds using the Langevin Diffusion |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Scalable and Adaptive Variational Bayes Methods for Hawkes Processes |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Scaling Capability in Token Space: An Analysis of Large Vision Language Model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scaling Data-Constrained Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scaling ResNets in the Large-depth Regime |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Score-Based Diffusion Models in Function Space |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Score-based Causal Representation Learning: Linear and General Transformations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Selective Inference with Distributed Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sharp Bounds for Sequential Federated Learning on Heterogeneous Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Simplex Constrained Sparse Optimization via Tail Screening |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sparse SVM with Hard-Margin Loss: a Newton-Augmented Lagrangian Method in Reduced Dimensions |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stabilizing Sharpness-Aware Minimization Through A Simple Renormalization Strategy |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Stable learning using spiking neural networks equipped with affine encoders and decoders |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical Inference of Random Graphs With a Surrogate Likelihood Function |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical field theory for Markov decision processes under uncertainty |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stochastic Interior-Point Methods for Smooth Conic Optimization with Applications |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Stochastic Interpolants: A Unifying Framework for Flows and Diffusions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Supervised Learning with Evolving Tasks and Performance Guarantees |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Talent: A Tabular Analytics and Learning Toolbox |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Test-Time Training on Video Streams |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| TorchCP: A Python Library for Conformal Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Towards Unified Native Spaces in Kernel Methods |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Transformers from Diffusion: A Unified Framework for Neural Message Passing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Unified Discrete Diffusion for Categorical Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Universal Online Convex Optimization Meets Second-order Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Uplift Model Evaluation with Ordinal Dominance Graphs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| VFOSA: Variance-Reduced Fast Operator Splitting Algorithms for Generalized Equations |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Variance-Aware Estimation of Kernel Mean Embedding |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| WEFE: A Python Library for Measuring and Mitigating Bias in Word Embeddings |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| gsplat: An Open-Source Library for Gaussian Splatting |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| skglm: Improving scikit-learn for Regularized Generalized Linear Models |
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✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |