47 citations · 272 across the 43 of their papers we have counts for
8 papers · 1 filter
A Generalized McDiarmid Inequality: Sample Reuse and Non-Uniform Unbounded Differences
Jeremiah Birrell
We develop a novel generalization of McDiarmid's concentration inequality and use it to obtain a uniform law of large numbers result and error bounds on stochastic optimization pro…
Statistical Guarantees for Distributionally Robust Optimization with Optimal Transport and OT-Regularized Divergences
Jeremiah Birrell, Xiaoxi Shen
We study finite-sample statistical performance guarantees for distributionally robust optimization (DRO) with optimal transport (OT) and OT-regularized divergence model neighborhoo…
Concentration Inequalities for Stochastic Optimization of Unbounded Objective Functions with Application to Denoising Score Matching
Jeremiah Birrell
We derive novel concentration inequalities that bound the statistical error for a large class of stochastic optimization problems, focusing on the case of unbounded objective funct…
Statistical Error Bounds for GANs with Nonlinear Objective Functionals
Jeremiah Birrell
Generative adversarial networks (GANs) are unsupervised learning methods for training a generator distribution to produce samples that approximate those drawn from a target distrib…
Nonlinear denoising score matching for enhanced learning of structured distributions
Jeremiah Birrell, Markos A. Katsoulakis, Luc Rey-Bellet +2
We present a novel method for training score-based generative models which uses nonlinear noising dynamics to improve learning of structured distributions. Generalizing to a nonlin…
Function-space regularized Rényi divergences
Jeremiah Birrell, Yannis Pantazis, Paul Dupuis +2
We propose a new family of regularized Rényi divergences parametrized not only by the order but also by a variational function space. These new objects are defined by taking th…