Andrew Jesson

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 32

Top Topics

Active Learning

Observational Data

Top Venues

Publications

Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
Estimating the Hallucination Rate of Generative AI. CoRR (2024)
Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg
Partial identification of dose responses with hidden confounders. UAI (2023)
Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. CoRR (2023)
Maëlys Solal, Andrew Jesson, Yarin Gal, Alyson Douglas
Using uncertainty-aware machine learning models to study aerosol-cloud interactions. CoRR (2023)
Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design. CoRR (2023)
Andrew Jesson, Chris Lu, Gunshi Gupta, Angelos Filos, Jakob Nicolaus Foerster, Yarin Gal
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. CoRR (2023)
Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal
BatchGFN: Generative Flow Networks for Batch Active Learning. CoRR (2023)
Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. ICML (2023)
Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
Differentiable Multi-Target Causal Bayesian Experimental Design. ICML (2023)
Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. Trans. Mach. Learn. Res. 2023 (2023)
Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. ICML (2023)
Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
Differentiable Multi-Target Causal Bayesian Experimental Design. CoRR (2023)
Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
Interventions, Where and How? Experimental Design for Causal Models at Scale. NeurIPS (2022)
Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. CoRR (2022)
Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
Interventions, Where and How? Experimental Design for Causal Models at Scale. CoRR (2022)
Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. NeurIPS (2022)
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
GeneDisco: A Benchmark for Experimental Design in Drug Discovery. ICLR (2022)
Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, Yarin Gal
Improving Deterministic Uncertainty Estimation in Deep Learning for Classification and Regression. CoRR (2021)
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS (2021)
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR (2021)
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab
GeneDisco: A Benchmark for Experimental Design in Drug Discovery. CoRR (2021)
Andrew Jesson, Peter Manshausen, Alyson Douglas, Duncan Watson-Parris, Yarin Gal, Philip Stier
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific. CoRR (2021)
Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. CoRR (2021)
Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. ICML (2021)
Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. NeurIPS (2020)
Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal
Identifying Causal Effect Inference Failure with Uncertainty-Aware Models. CoRR (2020)
Xiang Jiang, Liqiang Ding, Mohammad Havaei, Andrew Jesson, Stan Matwin
Task Adaptive Metric Space for Medium-Shot Medical Image Classification. MICCAI (1) (2019)
Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance. CoRR (2018)
Andrew Jesson, Cécile Low-Kam, Florian Soudan, Nicolas Chapados
Adversarially Learned Mixture Model. CoRR (2018)
Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, Stan Matwin
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification. CoRR (2018)
Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh, Damien Goblot, Florian Soudan, Nicolas Chapados
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance. MICCAI (3) (2017)
Andrew Jesson, Tal Arbel
Brain Tumor Segmentation Using a 3D FCN with Multi-scale Loss. BrainLes@MICCAI (2017)