Publications (24)
Using uncertainty-aware machine learning models to study aerosol-cloud interactions
Maëlys Solal, Andrew Jesson, Yarin Gal +1
Hypothesis Testing the Circuit Hypothesis in LLMs
Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret +5
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas, Mauricio Reyes, Andras Jakab +421
Estimating the Hallucination Rate of Generative AI
Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu +5
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
Clare Lyle, Arash Mehrjou, Pascal Notin +4
BatchGFN: Generative Flow Networks for Batch Active Learning
Shreshth A. Malik, Salem Lahlou, Andrew Jesson +5
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
Andrew Jesson, Chris Lu, Gunshi Gupta +4
Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale
Andrew Jesson, Yiding Jiang
Interventions, Where and How? Experimental Design for Causal Models at Scale
Panagiotis Tigas, Yashas Annadani, Andrew Jesson +3
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
Joost van Amersfoort, Lewis Smith, Andrew Jesson +2
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson +4
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
Andrew Jesson, Nicolas Guizard, Sina Hamidi Ghalehjegh +3
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding
Andrew Jesson, Sören Mindermann, Yarin Gal +1
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
Miruna Oprescu, Jacob Dorn, Marah Ghoummaid +3
Differentiable Multi-Target Causal Bayesian Experimental Design
Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova +4
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
Andrew Jesson, Alyson Douglas, Peter Manshausen +5
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian +3
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort +3
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models
Andrew Jesson, Sören Mindermann, Uri Shalit +1
Adversarially Learned Mixture Model
Andrew Jesson, Cécile Low-Kam, Tanya Nair +3
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
Andrew Jesson, Peter Manshausen, Alyson Douglas +3
Partial Identification of Dose Responses with Hidden Confounders
Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson +3
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Xiang Jiang, Mohammad Havaei, Gabriel Chartrand +5
Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective
Andrew Jesson, Nicolas Beltran-Velez, David Blei