Boltzmann equation solver adapted to emergent chemical non-equilibrium. - Jeremiah Birrell; Jon Wilkening; Johann Rafelski (J. Comput. Phys., 2015)

Robustness of Dynamical Quantities of Interest via Goal-Oriented Information Theory. - Jeremiah Birrell; Markos A. Katsoulakis; Luc Rey-Bellet (CoRR, 2019)

A Variational Formula for Rényi Divergences. - Jeremiah Birrell; Paul Dupuis; Markos A. Katsoulakis; Luc Rey-Bellet; Jie Wang 030 (CoRR, 2020)

(f, Γ)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics. - Jeremiah Birrell; Paul Dupuis; Markos A. Katsoulakis; Yannis Pantazis; Luc Rey-Bellet (CoRR, 2020)

Uncertainty Quantification for Markov Processes via Variational Principles and Functional Inequalities. - Jeremiah Birrell; Luc Rey-Bellet (SIAM/ASA J. Uncertain. Quantification, 2020)

Variational Representations and Neural Network Estimation of Rényi Divergences. - Jeremiah Birrell; Paul Dupuis; Markos A. Katsoulakis; Luc Rey-Bellet; Jie Wang 030 (SIAM J. Math. Data Sci., 2021)

Structure-preserving GANs. - Jeremiah Birrell; Markos A. Katsoulakis; Luc Rey-Bellet; Wei Zhu 007 (ICML, 2022)

(f, Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics. - Jeremiah Birrell; Paul Dupuis; Markos A. Katsoulakis; Yannis Pantazis; Luc Rey-Bellet (J. Mach. Learn. Res., 2022)

Optimizing Variational Representations of Divergences and Accelerating Their Statistical Estimation. - Jeremiah Birrell; Markos A. Katsoulakis; Yannis Pantazis (IEEE Trans. Inf. Theory, 2022)

Function-space regularized Rényi divergences. - Jeremiah Birrell; Yannis Pantazis; Paul Dupuis; Luc Rey-Bellet; Markos A. Katsoulakis (ICLR, 2023)

Adversarially Robust Deep Learning with Optimal-Transport-Regularized Divergences. - Jeremiah Birrell; Reza Ebrahimi 001 (CoRR, 2023)

Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement. - Jeremiah Birrell; Reza Ebrahimi 001; Rouzbeh Behnia; Jason Pacheco (NeurIPS, 2024)

Nonlinear denoising score matching for enhanced learning of structured distributions. - Jeremiah Birrell; Markos A. Katsoulakis; Luc Rey-Bellet; Benjamin J. Zhang; Wei Zhu 007 (CoRR, 2024)

Concentration Inequalities for (f,Ɣ)-GANs. - Jeremiah Birrell (CoRR, 2024)

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models. - Kasra Ahmadi; Rouzbeh Behnia; Reza Ebrahimi 001; Mehran Mozaffari Kermani; Jeremiah Birrell; Jason Pacheco; Attila A. Yavuz (SP (Workshops), 2025)

Statistical Error Bounds for GANs with Nonlinear Objective Functionals. - Jeremiah Birrell (Trans. Mach. Learn. Res., 2025)

Concentration Inequalities for the Stochastic Optimization of Unbounded Objectives with Application to Denoising Score Matching. - Jeremiah Birrell (CoRR, 2025)