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Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation


Jul 12, 2022
Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster

* ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in the Real World. 16 pages, 5 figures 

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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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Deep End-to-end Causal Inference


Feb 04, 2022
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang


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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

* 33 pages, 8 figures. Published as a conference paper at NeurIPS 2021 

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On Contrastive Representations of Stochastic Processes


Jun 18, 2021
Emile Mathieu, Adam Foster, Yee Whye Teh


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Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness


Jun 15, 2021
Adam Foster, Árpi Vezér, Craig A Glastonbury, Páidí Creed, Sam Abujudeh, Aaron Sim


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Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design


Mar 03, 2021
Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth


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Improving Transformation Invariance in Contrastive Representation Learning


Oct 19, 2020
Adam Foster, Rattana Pukdee, Tom Rainforth


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A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments


Nov 01, 2019
Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth


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Variational Estimators for Bayesian Optimal Experimental Design


Mar 13, 2019
Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah Goodman


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