9 citations · 16 across the 7 of their papers we have counts for
8 papers
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
Cas van der Oord, Matthias Sachs, Dávid Péter Kovács +2
Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic…
Efficient Numerical Algorithms for the Generalized Langevin Equation
Benedict Leimkuhler, Matthias Sachs
We study the design and implementation of numerical methods to solve the generalized Langevin equation (GLE) focusing on canonical sampling properties of numerical integrators. For…
Non-reversible Markov chain Monte Carlo for sampling of districting maps
Gregory Herschlag, Jonathan C. Mattingly, Matthias Sachs +1
Evaluating the degree of partisan districting (Gerrymandering) in a statistical framework typically requires an ensemble of districting plans which are drawn from a prescribed prob…
Posterior computation with the Gibbs zig-zag sampler
Matthias Sachs, Deborshee Sen, Jianfeng Lu +1
An intriguing new class of piecewise deterministic Markov processes (PDMPs) has recently been proposed as an alternative to Markov chain Monte Carlo (MCMC). In order to facilitate…
Hypocoercivity properties of adaptive Langevin dynamics
Benedict Leimkuhler, Matthias Sachs, Gabriel Stoltz
Adaptive Langevin dynamics is a method for sampling the Boltzmann-Gibbs distribution at prescribed temperature in cases where the potential gradient is subject to stochastic pertur…
Efficient posterior sampling for high-dimensional imbalanced logistic regression
Deborshee Sen, Matthias Sachs, Jianfeng Lu +1
High-dimensional data are routinely collected in many areas. We are particularly interested in Bayesian classification models in which one or more variables are imbalanced. Current…
Ergodic properties of quasi-Markovian generalized Langevin equations with configuration dependent noise and non-conservative force
Benedict Leimkuhler, Matthias Sachs
We discuss the ergodic properties of quasi-Markovian stochastic differential equations, providing general conditions that ensure existence and uniqueness of a smooth invariant dist…
Quadrature Points via Heat Kernel Repulsion
Jianfeng Lu, Matthias Sachs, Stefan Steinerberger
We discuss the classical problem of how to pick weighted points on a dimensional manifold so as to obtain a reasonable quadrature rule $$ \frac{1}{|M|}\int_{M}{f(x) dx} \si…