Jesson, Andrew

20 publications

ICLR 2025 Can Generative AI Solve Your In-Context Learning Problem? a Martingale Perspective Andrew Jesson, Nicolas Beltran-Velez, David Blei

NeurIPSW 2024 Can Generative AI Solve Your In-Context Learning Problem? a Martingale Perspective Andrew Jesson, Nicolas Beltran-Velez, David Blei

NeurIPSW 2024 Efficient Experimentation for Estimation of Continuous and Discrete Conditional Treatment Effects Muhammed Razzak, Panagiotis Tigas, Andrew Jesson, Yarin Gal, Uri Shalit

NeurIPS 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 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

ICMLW 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 Blei

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

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

ICMLW 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

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

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

ICML 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

UAI 2023 Partial Identification of Dose Responses with Hidden Confounders Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg

TMLR 2023 Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal

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

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 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 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

ICML 2021 Quantifying Ignorance in Individual-Level Causal-Effect Estimates Under Hidden Confounding Andrew Jesson, Sören Mindermann, Yarin Gal, Uri Shalit

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