JMLR · Scour

Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization

Sampling and Estimation on Manifolds using the Langevin Diffusion

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

Optimization Over a Probability Simplex

On Consistent Bayesian Inference from Synthetic Data

Learning causal graphs via nonlinear sufficient dimension reduction

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

Implicit vs Unfolded Graph Neural Networks

Distributed Stochastic Bilevel Optimization: Improved Complexity and Heterogeneity Analysis

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

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

Dynamic angular synchronization under smoothness constraints

GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia

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

Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability

On Inference for the Support Vector Machine

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

How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences

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

High-Dimensional L2-Boosting: Rate of Convergence

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