Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. - Benjamin Bloem-Reddy; Adam Foster 001; Emile Mathieu; Yee Whye Teh (UAI, 2018)

Variational Bayesian Optimal Experimental Design. - Adam Foster 001; Martin Jankowiak; Eli Bingham; Paul Horsfall; Yee Whye Teh; Tom Rainforth; Noah D. Goodman (NeurIPS, 2019)

Variational Estimators for Bayesian Optimal Experimental Design. - Adam Foster 001; Martin Jankowiak; Eli Bingham; Paul Horsfall; Yee Whye Teh; Tom Rainforth; Noah D. Goodman (CoRR, 2019)

A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. - Adam Foster 001; Martin Jankowiak; Matthew O'Meara; Yee Whye Teh; Tom Rainforth (AISTATS, 2020)

Improving Transformation Invariance in Contrastive Representation Learning. - Adam Foster 001; Rattana Pukdee; Tom Rainforth (ICLR, 2021)

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. - Adam Foster 001; Desi R. Ivanova; Ilyas Malik; Tom Rainforth (ICML, 2021)

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. - Desi R. Ivanova; Adam Foster 001; Steven Kleinegesse; Michael U. Gutmann; Thomas Rainforth (NeurIPS, 2021)

On Contrastive Representations of Stochastic Processes. - Emile Mathieu; Adam Foster 001; Yee Whye Teh (NeurIPS, 2021)

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness. - Adam Foster 001; Árpi Vezér; Craig A. Glastonbury; Páidí Creed; Samer Abujudeh; Aaron Sim (ICML, 2022)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Miltiadis Allamanis; Cheng Zhang 005 (CoRR, 2022)

Learning Instance-Specific Data Augmentations. - Ning Miao; Emile Mathieu; Yann Dubois; Tom Rainforth; Yee Whye Teh; Adam Foster 001; Hyunjik Kim (CoRR, 2022)

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation. - Desi R. Ivanova; Joel Jennings; Cheng Zhang 005; Adam Foster 001 (CoRR, 2022)

Unbiased MLMC Stochastic Gradient-Based Optimization of Bayesian Experimental Designs. - Takashi Goda; Tomohiko Hironaka; Wataru Kitade; Adam Foster 001 (SIAM J. Sci. Comput., 2022)

Prediction-Oriented Bayesian Active Learning. - Freddie Bickford Smith; Andreas Kirsch 002; Sebastian Farquhar; Yarin Gal; Adam Foster 001; Tom Rainforth (AISTATS, 2023)

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. - Desi R. Ivanova; Joel Jennings; Tom Rainforth; Cheng Zhang 005; Adam Foster 001 (ICML, 2023)

Learning Instance-Specific Augmentations by Capturing Local Invariances. - Ning Miao; Tom Rainforth; Emile Mathieu; Yann Dubois; Yee Whye Teh; Adam Foster 001; Hyunjik Kim (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)

Modern Bayesian Experimental Design. - Tom Rainforth; Adam Foster 001; Desi R. Ivanova; Freddie Bickford Smith (CoRR, 2023)

Making Better Use of Unlabelled Data in Bayesian Active Learning. - Freddie Bickford Smith; Adam Foster 001; Tom Rainforth (AISTATS, 2024)

Amortized Active Causal Induction with Deep Reinforcement Learning. - Yashas Annadani; Panagiotis Tigas; Stefan Bauer; Adam Foster 001 (NeurIPS, 2024)

Highly Accurate Real-space Electron Densities with Neural Networks. - Lixue Cheng; P. Bernát Szabó; Zeno Schätzle; Derk P. Kooi; Jonas Köhler 001; Klaas J. H. Giesbertz; Frank Noé; Jan Hermann; Paola Gori-Giorgi; Adam Foster 001 (CoRR, 2024)

Rethinking Aleatoric and Epistemic Uncertainty. - Freddie Bickford Smith; Jannik Kossen; Eleanor Trollope; Mark van der Wilk; Adam Foster 001; Tom Rainforth (CoRR, 2024)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Agrin Hilmkil; Joel Jennings; et al. (Trans. Mach. Learn. Res., 2024)

An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking. - Adam Foster 001; Zeno Schätzle; P. Bernát Szabó; Lixue Cheng; Jonas Köhler 001; Gino Cassella; Nicholas Gao; Jiawei Li; Frank Noé; Jan Hermann (CoRR, 2025)