Feature Learning in Finite-Width Bayesian Deep Linear Networks with Multiple Outputs and Convolutional Layers

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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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Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test

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On Inference for the Support Vector Machine

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Random Pruning Over-parameterized Neural Networks Can Improve Generalization: A Training Dynamics Analysis

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Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

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Implicit vs Unfolded Graph Neural Networks

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Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification

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GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia

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Dynamic angular synchronization under smoothness constraints

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Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems

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Wasserstein F-tests for Frechet regression on Bures-Wasserstein manifolds

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Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis

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Learning causal graphs via nonlinear sufficient dimension reduction

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On Consistent Bayesian Inference from Synthetic Data

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Optimization Over a Probability Simplex

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Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method

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Sampling and Estimation on Manifolds using the Langevin Diffusion

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Sharp Bounds for Sequential Federated Learning on Heterogeneous Data

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Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization

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