47 citations · 272 across the 43 of their papers we have counts for
48 papers
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…
Sensitivity of SDE Solutions to Perturbations of the Diffusion and Drift
Jeremiah Birrell
We develop a method for bounding the sensitivity of solutions to stochastic differential equations (SDEs) to changes in the drift, , and diffusion, , by using a combination o…
Information Theoretic Adversarial Training of Large Language Models
Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3
Large language models (LLMs) remain vulnerable to adversarial prompting despite advances in alignment and safety, often exhibiting harmful behaviors under novel attack strategies.…
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…
Local Differential Privacy for Federated Learning with Fixed Memory Usage and Per-Client Privacy
Rouzbeh Behnia, Jeremiah Birrell, Arman Riasi +3
Federated learning (FL) enables organizations to collaboratively train models without sharing their datasets. Despite this advantage, recent studies show that both client updates a…
Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels
Jeremiah Birrell, Reza Ebrahimi
We introduce ANTIDOTE, a new class of objectives for learning under noisy labels which are defined in terms of a relaxation over an information-divergence neighborhood. Using conve…