Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models

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Extremal graphical modeling with latent variables via convex optimization

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On the Approximation of Kernel functions

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Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response

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Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning

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Optimizing Data Collection for Machine Learning

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Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective

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Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

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Rank-one Convexification for Sparse Regression

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gsplat: An Open-Source Library for Gaussian Splatting

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Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming

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Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds

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Accelerating optimization over the space of probability measures

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Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data

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Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power

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Optimal Experiment Design for Causal Effect Identification

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Mean Aggregator is More Robust than Robust Aggregators under Label Poisoning Attacks on Distributed Heterogeneous Data

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The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond

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depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers

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The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise

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