244 citations · 351 across the 7 of their papers we have counts for
7 papers
The Vizier Gaussian Process Bandit Algorithm
Xingyou Song, Qiuyi Zhang, Chansoo Lee +8
Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a la…
Fast Approximate Determinants Using Rational Functions
Thomas Colthurst, Srinivas Vasudevan, James Lottes +1
We show how rational function approximations to the logarithm, such as , can be turned into fast algorithms for approximating the determina…
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov +4
Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data sp…
FunMC: A functional API for building Markov Chains
Pavel Sountsov, Alexey Radul, Srinivas Vasudevan
Constant-memory algorithms, also loosely called Markov chains, power the vast majority of probabilistic inference and machine learning applications today. A lot of progress has bee…
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon +3
Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions. However, when the geometry of the posterior is unfavorable, it may…
Simple, Distributed, and Accelerated Probabilistic Programming
Dustin Tran, Matthew Hoffman, Dave Moore +5
We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single…