Grant M. Rotskoff

According to our database, Grant M. Rotskoff authored at least 16 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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, February, 2025

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

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

Discrete generative diffusion models without stochastic differential equations: a tensor network approach.

Luke Causer, Grant M. Rotskoff, Juan P. Garrahan

CoRR, 2024

Energy Rank Alignment: Using Preference Optimization to Search Chemical Space at Scale.

Shriram Chennakesavalu, Frank Hu, Sebastian Ibarraran, Grant M. Rotskoff

CoRR, 2024

Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity.

Haoxuan Chen, Yinuo Ren, Lexing Ying, Grant M. Rotskoff

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Statistical Spatially Inhomogeneous Diffusion Inference.

Yinuo Ren, Yiping Lu, Lexing Ying, Grant M. Rotskoff

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2021

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods.

Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden

CoRR, 2021

Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization.

Grant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden

Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020

A mean-field analysis of two-player zero-sum games.

Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant M. Rotskoff, Joan Bruna

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Dynamical Central Limit Theorem for Shallow Neural Networks.

Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Global convergence of neuron birth-death dynamics.

Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden

CoRR, 2019

Neuron birth-death dynamics accelerates gradient descent and converges asymptotically.

Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks.

Grant M. Rotskoff, Eric Vanden-Eijnden

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018