Foster, Adam

21 publications

ICML 2025 Rethinking Aleatoric and Epistemic Uncertainty Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark Van Der Wilk, Adam Foster, Tom Rainforth

NeurIPS 2024 Amortized Active Causal Induction with Deep Reinforcement Learning Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster

ICMLW 2024 Amortized Active Causal Induction with Deep Reinforcement Learning Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster

TMLR 2024 Deep End-to-End Causal Inference Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang

AISTATS 2024 Making Better Use of Unlabelled Data in Bayesian Active Learning Freddie Bickford Smith, Adam Foster, Tom Rainforth

NeurIPSW 2024 Rethinking Aleatoric and Epistemic Uncertainty Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth

ICML 2023 CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster

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 Learning Instance-Specific Augmentations by Capturing Local Invariances Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim

AISTATS 2023 Prediction-Oriented Bayesian Active Learning Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth

NeurIPSW 2022 A Causal AI Suite for Decision-Making Emre Kiciman, Eleanor Wiske Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang

ICML 2022 Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness Adam Foster, Arpi Vezer, Craig A. Glastonbury, Paidi Creed, Samer Abujudeh, Aaron Sim

NeurIPSW 2022 Deep End-to-End Causal Inference Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

ICML 2021 Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design Adam Foster, Desi R Ivanova, Ilyas Malik, Tom Rainforth

NeurIPS 2021 Implicit Deep Adaptive Design: Policy-Based Experimental Design Without Likelihoods Desi R Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth

ICLR 2021 Improving Transformation Invariance in Contrastive Representation Learning Adam Foster, Rattana Pukdee, Tom Rainforth

NeurIPS 2021 On Contrastive Representations of Stochastic Processes Emile Mathieu, Adam Foster, Yee W. Teh

AISTATS 2020 A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments Adam Foster, Martin Jankowiak, Matthew O’Meara, Yee Whye Teh, Tom Rainforth

NeurIPS 2019 Variational Bayesian Optimal Experimental Design Adam Foster, Martin Jankowiak, Elias Bingham, Paul Horsfall, Yee Whye Teh, Thomas Rainforth, Noah Goodman

UAI 2018 Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh