Adam Foster

According to our database, Adam Foster authored at least 22 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Deep End-to-end Causal Inference.

Tomas Geffner, Javier Antorán, 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

Trans. Mach. Learn. Res., 2024

Highly Accurate Real-space Electron Densities with Neural Networks.

Lixue Cheng, P. Bernát Szabó, Zeno Schätzle, Derk Kooi, Jonas Köhler, Klaas J. H. Giesbertz, Frank Noé, Jan Hermann, Paola Gori-Giorgi, Adam Foster

CoRR, 2024

Amortized Active Causal Induction with Deep Reinforcement Learning.

Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster

CoRR, 2024

Making Better Use of Unlabelled Data in Bayesian Active Learning.

Freddie Bickford Smith, Adam Foster, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Modern Bayesian Experimental Design.

Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith

CoRR, 2023

Differentiable Multi-Target Causal Bayesian Experimental Design.

Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer

Proceedings of the International Conference on Machine Learning, 2023

Learning Instance-Specific Augmentations by Capturing Local Invariances.

Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim

Proceedings of the International Conference on Machine Learning, 2023

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design.

Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster

Proceedings of the International Conference on Machine Learning, 2023

Prediction-Oriented Bayesian Active Learning.

Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Unbiased MLMC Stochastic Gradient-Based Optimization of Bayesian Experimental Designs.

Takashi Goda, Tomohiko Hironaka, Wataru Kitade, Adam Foster

SIAM J. Sci. Comput., 2022

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation.

Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster

CoRR, 2022

Learning Instance-Specific Data Augmentations.

Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim

CoRR, 2022

Deep End-to-end Causal Inference.

Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

CoRR, 2022

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness.

Adam Foster, Árpi Vezér, Craig A. Glastonbury, Páidí Creed, Samer Abujudeh, Aaron Sim

Proceedings of the International Conference on Machine Learning, 2022

2021

On Contrastive Representations of Stochastic Processes.

Emile Mathieu, Adam Foster, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods.

Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design.

Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth

Proceedings of the 38th International Conference on Machine Learning, 2021

Improving Transformation Invariance in Contrastive Representation Learning.

Adam Foster, Rattana Pukdee, Tom Rainforth

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments.

Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Variational Estimators for Bayesian Optimal Experimental Design.

Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman

CoRR, 2019

Variational Bayesian Optimal Experimental Design.

Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks.

Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018