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Andrew Jesson

PhD, started 2019

Andrew is a DPhil student in the Department of Computer Science at the University of Oxford. He works in the Applied and Theoretical Machine Learning Group (OATML) under the supervision of Yarin Gal. Prior to joining the group, he was a program manager and researcher at Imagia in Montreal. He obtained his undergraduate and master’s degrees from the Department of Electrical and Computer Engineering at McGill University, working in the Probabilistic Vision Group (PVG) with Tal Arbel. He is interested in the application of machine learning to facilitate scientific discovery and creativity.

Publications while at OATML:

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

We study the problem of learning conditional average treatment effects (CATE) from high-dimensional, observational data with unobserved confounders. Unobserved confounders introduce ignorance -- a level of unidentifiability -- about an individual's response to treatment by inducing bias in CATE estimates. We present a new parametric interval estimator suited for high-dimensional data, that estimates a range of possible CATE values when given a predefined bound on the level of hidden confounding. Further, previous interval estimators do not account for ignorance about the CATE stemming from samples that may be underrepresented in the original study, or samples that violate the overlap assumption. Our novel interval estimator also incorporates model uncertainty so that practitioners can be made aware of out-of-distribution data. We prove that our estimator converges to tight bounds on CATE when there may be unobserved confounding, and assess it using semi-synthetic, high-dimensional ... [full abstract]

Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
ICML, 2021

Identifying Causal Effect Inference Failure with Uncertainty-Aware Models

Recommending the best course of action for an individual is a major application of individual-level causal effect estimation. This application is often needed in safety-critical domains such as healthcare, where estimating and communicating uncertainty to decision-makers is crucial. We introduce a practical approach for integrating uncertainty estimation into a class of state-of-the-art neural network methods used for individual-level causal estimates. We show that our methods enable us to deal gracefully with situations of "no-overlap", common in high-dimensional data, where standard applications of causal effect approaches fail. Further, our methods allow us to handle covariate shift, where test distribution differs to train distribution, common when systems are deployed in practice. We show that when such a covariate shift occurs, correctly modeling uncertainty can keep us from giving overconfident and potentially harmful recommendations. We demonstrate our methodology with a ra... [full abstract]

Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
NeurIPS, 2020
[arXiv] [BibTex]
More publications on Google Scholar.

Blog Posts

21 OATML Conference and Workshop papers at ICML 2021

OATML group members and collaborators are proud to present 21 papers at ICML 2021, including 7 papers at the main conference and 14 papers at various workshops. Group members will also be giving invited talks and participate in panel discussions at the workshops. …

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Angelos Filos, Clare Lyle, Jannik Kossen, Sebastian Farquhar, Tom Rainforth, Andrew Jesson, Sören Mindermann, Tim G. J. Rudner, Oscar Key, Binxin (Robin) Ru, Pascal Notin, Panagiotis Tigas, Andreas Kirsch, Jishnu Mukhoti, Joost van Amersfoort, Lisa Schut, Muhammed Razzak, Aidan Gomez, Jan Brauner, Yarin Gal, 17 Jul 2021

When causal inference fails - detecting violated assumptions with uncertainty-aware models

NeurIPS 2020. Tl;dr: Uncertainty-aware deep models can identify when some causal-effect inference assumptions are violated.

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Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal, 08 Dec 2020

22 OATML Conference and Workshop papers at NeurIPS 2020

OATML group members and collaborators are proud to be presenting 22 papers at NeurIPS 2020. Group members are also co-organising various events around NeurIPS, including workshops, the NeurIPS Meet-Up on Bayesian Deep Learning and socials. …

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Muhammed Razzak, Panagiotis Tigas, Angelos Filos, Atılım Güneş Baydin, Andrew Jesson, Andreas Kirsch, Clare Lyle, Freddie Kalaitzis, Jan Brauner, Jishnu Mukhoti, Lewis Smith, Lisa Schut, Mizu Nishikawa-Toomey, Oscar Key, Binxin (Robin) Ru, Sebastian Farquhar, Sören Mindermann, Tim G. J. Rudner, Yarin Gal, 04 Dec 2020

Are you looking to do a PhD in machine learning? Did you do a PhD in another field and want to do a postdoc in machine learning? Would you like to visit the group?

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