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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Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick

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Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables

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Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions

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Regularizing Hard Examples Improves Adversarial Robustness

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Random ReLU Neural Networks as Non-Gaussian Processes

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Riemannian Bilevel Optimization

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Supervised Learning with Evolving Tasks and Performance Guarantees

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Error estimation and adaptive tuning for unregularized robust M-estimator

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From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective

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Locally Private Causal Inference for Randomized Experiments

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Estimating Network-Mediated Causal Effects via Principal Components Network Regression

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Selective Inference with Distributed Data

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Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization

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