3 papers
stat.ML2026
LatentFlow: A General Framework for Conditioning Stochastic Processes
Louis Sharrock, Lachlan Astfalck, Henry Moss
Stochastic-process models are, as a rule, far easier to simulate than to condition. Non-linear observations, non-Gaussian likelihoods, black-box information, and global constraints…
cs.LG2025
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…
cs.LG2025
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…