Grant M. Rotskoff

Orcid: 0000-0002-7772-5179

According to our database1, Grant M. Rotskoff authored at least 16 papers between 2018 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms.
CoRR, February, 2025

2024
Features are fate: a theory of transfer learning in high-dimensional regression.
CoRR, 2024

How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework.
CoRR, 2024

Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning.
CoRR, 2024

Discrete generative diffusion models without stochastic differential equations: a tensor network approach.
CoRR, 2024

Energy Rank Alignment: Using Preference Optimization to Search Chemical Space at Scale.
CoRR, 2024

Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Statistical Spatially Inhomogeneous Diffusion Inference.
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.
CoRR, 2021

Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalization.
Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020
A mean-field analysis of two-player zero-sum games.
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.
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.
CoRR, 2019

Neuron birth-death dynamics accelerates gradient descent and converges asymptotically.
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.
CoRR, 2018

Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018


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