DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

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Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

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Manifold Fitting under Unbounded Noise

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Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play

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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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