Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks. - Grant M. Rotskoff; Eric Vanden-Eijnden (NeurIPS, 2018)

Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error. - Grant M. Rotskoff; Eric Vanden-Eijnden (CoRR, 2018)

Neuron birth-death dynamics accelerates gradient descent and converges asymptotically. - Grant M. Rotskoff; Samy Jelassi; Joan Bruna; Eric Vanden-Eijnden (ICML, 2019)

Global convergence of neuron birth-death dynamics. - Grant M. Rotskoff; Samy Jelassi; Joan Bruna; Eric Vanden-Eijnden (CoRR, 2019)

A Dynamical Central Limit Theorem for Shallow Neural Networks. - Zhengdao Chen; Grant M. Rotskoff; Joan Bruna; Eric Vanden-Eijnden (NeurIPS, 2020)

A mean-field analysis of two-player zero-sum games. - Carles Domingo-Enrich; Samy Jelassi; Arthur Mensch; Grant M. Rotskoff; Joan Bruna (NeurIPS, 2020)

Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization. - Grant M. Rotskoff; Andrew R. Mitchell; Eric Vanden-Eijnden (MSML, 2021)

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods. - Marylou Gabrié; Grant M. Rotskoff; Eric Vanden-Eijnden (CoRR, 2021)

Statistical Spatially Inhomogeneous Diffusion Inference. - Yinuo Ren; Yiping Lu 001; Lexing Ying; Grant M. Rotskoff (AAAI, 2024)

Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity. - Haoxuan Chen; Yinuo Ren; Lexing Ying; Grant M. Rotskoff (NeurIPS, 2024)

Energy Rank Alignment: Using Preference Optimization to Search Chemical Space at Scale. - Shriram Chennakesavalu; Frank Hu; Sebastian Ibarraran; Grant M. Rotskoff (CoRR, 2024)

Discrete generative diffusion models without stochastic differential equations: a tensor network approach. - Luke Causer; Grant M. Rotskoff; Juan P. Garrahan (CoRR, 2024)

Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning. - Frank Hu; Michael S. Chen; Grant M. Rotskoff; Matthew W. Kanan; Thomas E. Markland (CoRR, 2024)

Features are fate: a theory of transfer learning in high-dimensional regression. - Javan Tahir; Surya Ganguli; Grant M. Rotskoff (CoRR, 2024)

How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework. - Yinuo Ren; Haoxuan Chen; Grant M. Rotskoff; Lexing Ying (ICLR, 2025)

Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms. - Yinuo Ren; Haoxuan Chen; Yuchen Zhu; Wei Guo; Yongxin Chen; Grant M. Rotskoff; Molei Tao; Lexing Ying (CoRR, 2025)

A Unified Approach to Analysis and Design of Denoising Markov Models. - Yinuo Ren; Grant M. Rotskoff; Lexing Ying (CoRR, 2025)