Brain Tumor Segmentation Using a 3D FCN with Multi-scale Loss. - Andrew Jesson; Tal Arbel (BrainLes@MICCAI, 2017)

CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance. - Andrew Jesson; Nicolas Guizard; Sina Hamidi Ghalehjegh; Damien Goblot; Florian Soudan; Nicolas Chapados (MICCAI (3), 2017)

On the Importance of Attention in Meta-Learning for Few-Shot Text Classification. - Xiang Jiang 001; Mohammad Havaei; Gabriel Chartrand; Hassan Chouaib; Thomas Vincent; Andrew Jesson; Nicolas Chapados; Stan Matwin (CoRR, 2018)

Adversarially Learned Mixture Model. - Andrew Jesson; Cécile Low-Kam; Florian Soudan; Nicolas Chapados (CoRR, 2018)

Task Adaptive Metric Space for Medium-Shot Medical Image Classification. - Xiang Jiang 001; Liqiang Ding; Mohammad Havaei; Andrew Jesson; Stan Matwin (MICCAI (1), 2019)

Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models. - Andrew Jesson; Sören Mindermann; Uri Shalit; Yarin Gal (NeurIPS, 2020)

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. - Andrew Jesson; Sören Mindermann; Yarin Gal; Uri Shalit (ICML, 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 002; Uri Shalit; Yarin Gal (NeurIPS, 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)

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)

GeneDisco: A Benchmark for Experimental Design in Drug Discovery. - Arash Mehrjou; Ashkan Soleymani; Andrew Jesson; Pascal Notin; Yarin Gal; Stefan Bauer; Patrick Schwab (ICLR, 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 (NeurIPS, 2022)

Interventions, Where and How? Experimental Design for Causal Models at Scale. - Panagiotis Tigas; Yashas Annadani; Andrew Jesson; Bernhard Schölkopf; Yarin Gal; Stefan Bauer (NeurIPS, 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)

DiscoBAX: Discovery of optimal intervention sets in genomic experiment design. - Clare Lyle; Arash Mehrjou; Pascal Notin; Andrew Jesson; Stefan Bauer; Yarin Gal; Patrick Schwab (ICML, 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 (ICML, 2023)

Differentiable Multi-Target Causal Bayesian Experimental Design. - Panagiotis Tigas; Yashas Annadani; Desi R. Ivanova; Andrew Jesson; Yarin Gal; Adam Foster 001; Stefan Bauer (ICML, 2023)

Partial identification of dose responses with hidden confounders. - Myrl G. Marmarelis; Elizabeth Haddad; Andrew Jesson; Neda Jahanshad; Aram Galstyan; Greg Ver Steeg (UAI, 2023)

Using uncertainty-aware machine learning models to study aerosol-cloud interactions. - Maëlys Solal; Andrew Jesson; Yarin Gal; Alyson Douglas (CoRR, 2023)

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. - Andrew Jesson; Chris Lu 001; Gunshi Gupta; Angelos Filos; Jakob Nicolaus Foerster; Yarin Gal (CoRR, 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)

Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. - Andreas Kirsch 002; Sebastian Farquhar; Parmida Atighehchian; Andrew Jesson; Frédéric Branchaud-Charron; Yarin Gal (Trans. Mach. Learn. Res., 2023)

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages. - Andrew Jesson; Chris Lu 001; Gunshi Gupta; Nicolas Beltran-Velez; Angelos Filos; Jakob Nicolaus Foerster; Yarin Gal (ICML, 2024)

Estimating the Hallucination Rate of Generative AI. - Andrew Jesson; Nicolas Beltran-Velez; Quentin Chu; Sweta Karlekar; Jannik Kossen; Yarin Gal; John P. Cunningham; David M. Blei (NeurIPS, 2024)

Hypothesis Testing the Circuit Hypothesis in LLMs. - Claudia Shi; Nicolas Beltran-Velez; Achille Nazaret; Carolina Zheng; Adrià Garriga-Alonso; Andrew Jesson; Maggie Makar; David M. Blei (NeurIPS, 2024)

Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale. - Andrew Jesson; Yiding Jiang (CoRR, 2024)

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective. - Andrew Jesson; Nicolas Beltran-Velez; David M. Blei (ICLR, 2025)