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Andrew Jesson
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Differentiable Multi-Target Causal Bayesian Experimental Design


Feb 21, 2023
Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer

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Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions


Apr 26, 2022
Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit

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* 22 pages 
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Interventions, Where and How? Experimental Design for Causal Models at Scale


Mar 03, 2022
Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer

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Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data


Nov 03, 2021
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal

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* 24 pages, 8 Figures, 5 tables, NeurIPS 2021 
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Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific


Nov 03, 2021
Andrew Jesson, Peter Manshausen, Alyson Douglas, Duncan Watson-Parris, Yarin Gal, Philip Stier

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GeneDisco: A Benchmark for Experimental Design in Drug Discovery


Oct 22, 2021
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab

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Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding


Mar 08, 2021
Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit

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* 18 pages, 5 figures, In review 
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Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression


Feb 22, 2021
Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal

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