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Tom Rainforth
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Modern Bayesian Experimental Design


Feb 28, 2023
Tom Rainforth, Adam Foster, Desi R Ivanova, Freddie Bickford Smith

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CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design


Feb 27, 2023
Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster

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* 9 pages, 6 figures 
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Do Bayesian Neural Networks Need To Be Fully Stochastic?


Nov 11, 2022
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, Tom Rainforth

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Learning Instance-Specific Data Augmentations


May 31, 2022
Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim

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A Continuous Time Framework for Discrete Denoising Models


May 30, 2022
Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, Arnaud Doucet

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* 41 pages, 12 figures 
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Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation


Feb 14, 2022
Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth

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Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods


Nov 03, 2021
Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth

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* 33 pages, 8 figures. Published as a conference paper at NeurIPS 2021 
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Online Variational Filtering and Parameter Learning


Oct 26, 2021
Andrew Campbell, Yuyang Shi, Tom Rainforth, Arnaud Doucet

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* 27 pages, 6 figures. NeurIPS 2021 (Oral) 
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Learning Multimodal VAEs through Mutual Supervision


Jul 01, 2021
Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, N. Siddharth

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InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents


Jun 25, 2021
Ning Miao, Emile Mathieu, N. Siddharth, Yee Whye Teh, Tom Rainforth

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