4 papers · 1 filter
Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
Jixiang Qing, Henry Moss, Matthias Sachs
We consider the active learning problem where the goal is to learn an unknown function with low prediction error under an unknown Boltzmann distribution induced by the function its…
Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids
Mads Greisen Højlund, August Smart Lykke-Møller, Henry Moss +1
We introduce CUTS-GPR, a new method for performing numerically exact Gaussian process regression (GPR) in high-dimensional settings. The key component of CUTS-GPR is an extremely f…
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…