Grant M. Rotskoff

27 papers A* 8Journal 18Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	The cost of quantum algorithms for biochemistry: A case study in metaphosphate hydrolysis. CoRR Ryan LaRose, Alan Bidart, Ben DalFavero, Sophia E. Economou, J. Wayne Mullinax, Mafalda Ramôa, Jeremiah Rowland, Brenda Rubenstein, Nicolas P. D. Sawaya, Prateek Vaish, Grant M. Rotskoff, Norm M. Tubman
2025	J	`jnl`	A Unified Approach to Analysis and Design of Denoising Markov Models. CoRR Yinuo Ren, Grant M. Rotskoff, Lexing Ying
2025	J	`jnl`	DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models. CoRR Yinuo Ren, Wenhao Gao, Lexing Ying, Grant M. Rotskoff, Jiequn Han
2025	J	`jnl`	Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms. CoRR Yinuo Ren, Haoxuan Chen, Yuchen Zhu, Wei Guo, Yongxin Chen, Grant M. Rotskoff, Molei Tao, Lexing Ying
2025	A*	`conf`	Features are fate: a theory of transfer learning in high-dimensional regression. ICML Javan Tahir, Surya Ganguli, Grant M. Rotskoff
2025	A*	`conf`	How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework. ICLR Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff, Lexing Ying
2025	J	`jnl`	Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence. CoRR Frank Hu, Jonathan M. Tubb, Dimitris Argyropoulos, Sergey Golotvin, Mikhail E. Elyashberg, Grant M. Rotskoff, Matthew W. Kanan, Thomas E. Markland
2025	J	`jnl`	The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS). CoRR Andrew Ferguson, Marisa Lafleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille L. Bilodeau, Keith Brown, Emmanuel J. Candès, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor W. Coley, Andrew J. Connolly, Fred Daum, Sijia S. Dong, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos, Nicolás García Trillos, Cecilia Garraffo, Robert Ghrist, Rafael Gómez-Bombarelli, Gianluca Guadagni, Sreelekha Guggilam, Sergei Gukov, Juan B. Gutierrez, Salman Habib, Johannes Hachmann, Boris Hanin, Philip C. Harris, Murray Holland, Elizabeth Holm, Hsin-Yuan Huang, Shih-Chieh Hsu, Nick Jackson, Olexandr Isayev, Heng Ji, Aggelos K. Katsaggelos, Jeremy Kepner, Yannis G. Kevrekidis, Michelle P. Kuchera, J. Nathan Kutz, Branislava Lalic, Ann Lee, Matt LeBlanc, Josiah Lim, Rebecca Lindsey, Yongmin Liu, Peter Y. Lu, Sudhir Malik, Vuk Mandic, Vidya B. Manian, Emeka P. Mazi, Pankaj Mehta, Peter Melchior, Brice Ménard, Jennifer Ngadiuba, Stella Offner, Elsa Olivetti, Shyue Ping Ong, Christopher Rackauckas, Philippe Rigollet, Chad Risko, Philip Romero, Grant M. Rotskoff, Brett Savoie, Uros Seljak, David Shih, Gary Shiu, Dima Shlyakhtenko, Eva Silverstein, Taylor Sparks, Thomas Strohmer, Christopher Stubbs, Stephen Thomas, Suriyanarayanan Vaikuntanathan, René Vidal, Francisco Villaescusa-Navarro, Gregory Voth, Benjamin Wandelt, Rachel Ward, Melanie Weber, Risa Wechsler, Stephen Whitelam, Olaf Wiest, Mike Williams, Zhuoran Yang, Yaroslava G. Yingling, Bin Yu, Shuwen Yue, Ann Zabludoff, Huimin Zhao, Tong Zhang
2024	A*	`conf`	Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity. NeurIPS Haoxuan Chen, Yinuo Ren, Lexing Ying, Grant M. Rotskoff
2024	J	`jnl`	Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity. CoRR Haoxuan Chen, Yinuo Ren, Lexing Ying, Grant M. Rotskoff
2024	J	`jnl`	Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning. CoRR Frank Hu, Michael S. Chen, Grant M. Rotskoff, Matthew W. Kanan, Thomas E. Markland
2024	J	`jnl`	Discrete generative diffusion models without stochastic differential equations: a tensor network approach. CoRR Luke Causer, Grant M. Rotskoff, Juan P. Garrahan
2024	J	`jnl`	Energy Rank Alignment: Using Preference Optimization to Search Chemical Space at Scale. CoRR Shriram Chennakesavalu, Frank Hu, Sebastian Ibarraran, Grant M. Rotskoff
2024	J	`jnl`	Features are fate: a theory of transfer learning in high-dimensional regression. CoRR Javan Tahir, Surya Ganguli, Grant M. Rotskoff
2024	J	`jnl`	How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework. CoRR Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff, Lexing Ying
2024	A*	`conf`	Statistical Spatially Inhomogeneous Diffusion Inference. AAAI Yinuo Ren, Yiping Lu, Lexing Ying, Grant M. Rotskoff
2023	J	`jnl`	Statistical Spatially Inhomogeneous Diffusion Inference. CoRR Yinuo Ren, Yiping Lu, Lexing Ying, Grant M. Rotskoff
2021	—	`conf`	Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization. MSML Grant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden
2021	J	`jnl`	Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods. CoRR Marylou Gabrié, Grant M. Rotskoff, Eric Vanden-Eijnden
2020	A*	`conf`	A Dynamical Central Limit Theorem for Shallow Neural Networks. NeurIPS Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden
2020	J	`jnl`	A Dynamical Central Limit Theorem for Shallow Neural Networks. CoRR Zhengdao Chen, Grant M. Rotskoff, Joan Bruna, Eric Vanden-Eijnden
2020	A*	`conf`	A mean-field analysis of two-player zero-sum games. NeurIPS Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant M. Rotskoff, Joan Bruna
2020	J	`jnl`	A mean-field analysis of two-player zero-sum games. CoRR Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant M. Rotskoff, Joan Bruna
2019	J	`jnl`	Global convergence of neuron birth-death dynamics. CoRR Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden
2019	A*	`conf`	Neuron birth-death dynamics accelerates gradient descent and converges asymptotically. ICML Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden
2018	J	`jnl`	Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error. CoRR Grant M. Rotskoff, Eric Vanden-Eijnden
2018	A*	`conf`	Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks. NeurIPS Grant M. Rotskoff, Eric Vanden-Eijnden