Andrew Jesson

According to our database, Andrew Jesson authored at least 21 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

BatchGFN: Generative Flow Networks for Batch Active Learning.

Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal

CoRR, 2023

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages.

Andrew Jesson, Chris Lu, Gunshi Gupta, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal

CoRR, 2023

Using uncertainty-aware machine learning models to study aerosol-cloud interactions.

Maëlys Solal, Andrew Jesson, Yarin Gal, Alyson Douglas

CoRR, 2023

Partial identification of dose responses with hidden confounders.

Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Differentiable Multi-Target Causal Bayesian Experimental Design.

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

Proceedings of the International Conference on Machine Learning, 2023

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding.

Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit

Proceedings of the International Conference on Machine Learning, 2023

DiscoBAX: Discovery of optimal intervention sets in genomic experiment design.

Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab

Proceedings of the International Conference on Machine Learning, 2023

2022

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

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

CoRR, 2022

Interventions, Where and How? Experimental Design for Causal Models at Scale.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

GeneDisco: A Benchmark for Experimental Design in Drug Discovery.

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific.

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

CoRR, 2021

Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression.

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

CoRR, 2021

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

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models.

Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Task Adaptive Metric Space for Medium-Shot Medical Image Classification.

Xiang Jiang, Liqiang Ding, Mohammad Havaei, Andrew Jesson, Stan Matwin

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

2018

Adversarially Learned Mixture Model.

Andrew Jesson, Cécile Low-Kam, Florian Soudan, Nicolas Chapados

CoRR, 2018

On the Importance of Attention in Meta-Learning for Few-Shot Text Classification.

Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, Stan Matwin

CoRR, 2018

2017

CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance.

Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017

Brain Tumor Segmentation Using a 3D FCN with Multi-scale Loss.

Andrew Jesson, Tal Arbel

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2017