2 citations · 2 across the 3 of their papers we have counts for
4 papers
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…
Conditioning Gaussian Processes on Almost Anything
Henry Moss, Lachlan Astfalck, Thomas Cowperthwaite +5
Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We establish an explicit equivalenc…
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…
Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization
Colin Doumont, Victor Picheny, Viacheslav Borovitskiy +1
Bayesian Optimization (BO) has the potential to solve various combinatorial tasks, ranging from materials science to neural architecture search. However, BO requires specialized ke…