Rainforth, Thomas

11 publications

NeurIPS 2023 Deep Stochastic Processes via Functional Markov Transition Operators Jin Xu, Emilien Dupont, Kaspar Märtens, Thomas Rainforth, Yee Whye Teh

NeurIPS 2023 Trans-Dimensional Generative Modeling via Jump Diffusion Models Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Thomas Rainforth, Arnaud Doucet

NeurIPS 2022 A Continuous Time Framework for Discrete Denoising Models Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, Arnaud Doucet

NeurIPS 2022 Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation Jannik Kossen, Sebastian Farquhar, Yarin Gal, Thomas Rainforth

NeurIPS 2022 Rethinking Variational Inference for Probabilistic Programs with Stochastic Support Tim Reichelt, Luke Ong, Thomas Rainforth

NeurIPS 2021 Group Equivariant Subsampling Jin Xu, Hyunjik Kim, Thomas Rainforth, Yee W. Teh

NeurIPS 2021 Implicit Deep Adaptive Design: Policy-Based Experimental Design Without Likelihoods Desi R Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth

NeurIPS 2021 Online Variational Filtering and Parameter Learning Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet

NeurIPS 2021 Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Jannik Kossen, Neil Band, Clare Lyle, Aidan N Gomez, Thomas Rainforth, Yarin Gal

NeurIPS 2019 On the Fairness of Disentangled Representations Francesco Locatello, Gabriele Abbati, Thomas Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem

NeurIPS 2019 Variational Bayesian Optimal Experimental Design Adam Foster, Martin Jankowiak, Elias Bingham, Paul Horsfall, Yee Whye Teh, Thomas Rainforth, Noah Goodman