Rik Knowles — accepted AI-conference papers

2026

Scaling Bayesian Experimental Design to High-Dimensions with Information-Guided Diffusion

Adhi Saravanan, Rik Knowles, Gavin Kerrigan, Tom Rainforth

ICLR 2026poster

We present DiffBED, a Bayesian experimental design (BED) approach that scales to problems with high-dimensional design spaces. Our key insight is that current BED approaches typically cannot be scaled to real high--dimensional design problems because of the need to specify a likelihood model that re…

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2026

Tractable Expected Information Gains for Exponential Family Posteriors

Rik Knowles, Tom Rainforth

ICML 2026poster

We investigate which models admit a collapse of the expected information gain (EIG) and its derivative from a doubly intractable to a singly intractable expression. We prove that a sufficient condition is that the posterior distribution belongs to an exponential family (EF) that depends on the exper…

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