2 citations · 2 across the 4 of their papers we have counts for
4 papers · 1 filter
Search at the Cost of Sampling: Nearly-Instant Latent Space Bayesian Optimization
Donney Fan, Colin Doumont, Aleksandra Kalisz +4
Generative models are increasingly central to many de novo discovery pipelines, in which designs are generated at scale and filtered through virtual screens to determine a set of c…
Adaptive Candidate Point Thompson Sampling for High-Dimensional Bayesian Optimization
Donney Fan, Geoff Pleiss
In Bayesian optimization, Thompson sampling selects the evaluation point by sampling from the posterior distribution over the objective function maximizer. Because this sampling pr…
We Still Don't Understand High-Dimensional Bayesian Optimization
Colin Doumont, Donney Fan, Natalie Maus +3
Existing high-dimensional Bayesian optimization (BO) methods aim to overcome the curse of dimensionality by carefully encoding structural assumptions, from locality to sparsity to…
Asymmetric Duos: Sidekicks Improve Uncertainty
Tim G. Zhou, Evan Shelhamer, Geoff Pleiss
The go-to strategy to apply deep networks in settings where uncertainty informs decisions--ensembling multiple training runs with random initializations--is ill-suited for the extr…