1 code implementation • 24 Apr 2023 • Mateo Díaz, Ethan N. Epperly, Zachary Frangella, Joel A. Tropp, Robert J. Webber
This paper introduces two randomized preconditioning techniques for robustly solving kernel ridge regression (KRR) problems with a medium to large number of data points ($10^4 \leq N \leq 10^7$).
no code implementations • 11 Nov 2022 • huan zhang, Robert J. Webber, Michael Lindsey, Timothy C. Berkelbach, Jonathan Weare
The use of neural network parametrizations to represent the ground state in variational Monte Carlo (VMC) calculations has generated intense interest in recent years.
1 code implementation • 13 Jul 2022 • Yifan Chen, Ethan N. Epperly, Joel A. Tropp, Robert J. Webber
The randomly pivoted partial Cholesky algorithm (RPCholesky) computes a factorized rank-k approximation of an N x N positive-semidefinite (psd) matrix.
no code implementations • 19 Jun 2021 • Robert J. Webber, Michael Lindsey
Second, in order to realize this favorable comparison in the presence of stochastic noise, we analyze the effect of sampling error on VMC parameter updates and experimentally demonstrate that it can be reduced by the parallel tempering method.
no code implementations • 15 Feb 2021 • Justin Finkel, Robert J. Webber, Dorian S. Abbot, Edwin P. Gerber, Jonathan Weare
We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics.
Atmospheric and Oceanic Physics Dynamical Systems Data Analysis, Statistics and Probability
2 code implementations • 27 Nov 2020 • Robert J. Webber, David Aristoff, Gideon Simpson
We explore whether splitting and killing methods can improve the accuracy of Markov chain Monte Carlo (MCMC) estimates of rare event probabilities, and we make three contributions.
Numerical Analysis Numerical Analysis 65C05, 65C40, 82C80