32 citations · 107 across the 10 of their papers we have counts for
10 papers
Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensembles
Xinzhu Liang, Joseph M. Lukens, Sanjaya Lohani +4
This work introduces a new method designed for Bayesian deep learning called scalable Bayesian Monte Carlo (SBMC). The method is comprised of a model and an algorithm. The model in…
Unorthodox parallelization for Bayesian quantum state estimation
Hanson H. Nguyen, Kody J. H. Law, Joseph M. Lukens
Quantum state tomography (QST) allows for the reconstruction of quantum states through measurements and some inference technique under the assumption of repeated state preparations…
Comparison of parallel SMC and MCMC for Bayesian deep learning
Xinzhu Liang, Joseph M. Lukens, Sanjaya Lohani +4
This work systematically compares parallel implementations of consistent (asymptotically unbiased) Bayesian deep learning algorithms: sequential Monte Carlo sampler (SMC$_\parallel…
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
Shangda Yang, Vitaly Zankin, Maximilian Balandat +4
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.…
Geometric stabilization of extended S=2 vortices in two-dimensional photonic lattices: theoretical analysis, numerical computation and experimental results
K. J. H. Law, D. Song, P. G. Kevrekidis +2
In this work, we focus our studies on the subject of nonlinear discrete self-trapping of S=2 (doubly-charged) vortices in two-dimensional photonic lattices, including theoretical a…
Manipulation of Vortices by Localized Impurities in Bose-Einstein Condensates
M. C. Davis, R Carretero-González, Z. Shi +3
We consider the manipulation of Bose-Einstein condensate vortices by optical potentials generated by focused laser beams. It is shown that for appropriate choices of the laser stre…