5 citations · 7 across the 4 of their papers we have counts for
4 papers
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process…
Accelerating Metropolis-Hastings with Lightweight Inference Compilation
Feynman Liang, Nimar Arora, Nazanin Tehrani +3
In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in an open-sou…
PPL Bench: Evaluation Framework For Probabilistic Programming Languages
Sourabh Kulkarni, Kinjal Divesh Shah, Nimar Arora +9
We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and eva…
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methods
Nimar S. Arora, Nazanin Khosravani Tehrani, Kinjal Divesh Shah +7
Single-site Markov Chain Monte Carlo (MCMC) is a variant of MCMC in which a single coordinate in the state space is modified in each step. Structured relational models are a good c…