Adam Foster - RankMe

37 papers A* 11A 4Journal 22

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking. CoRR Adam Foster, Zeno Schätzle, P. Bernát Szabó, Lixue Cheng, Jonas Köhler, Gino Cassella, Nicholas Gao, Jiawei Li, Frank Noé, Jan Hermann
2025	A*	`conf`	Rethinking Aleatoric and Epistemic Uncertainty. ICML Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth
2024	A*	`conf`	Amortized Active Causal Induction with Deep Reinforcement Learning. NeurIPS Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster
2024	J	`jnl`	Amortized Active Causal Induction with Deep Reinforcement Learning. CoRR Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster
2024	J	`jnl`	Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 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
2024	J	`jnl`	Highly Accurate Real-space Electron Densities with Neural Networks. CoRR Lixue Cheng, P. Bernát Szabó, Zeno Schätzle, Derk P. Kooi, Jonas Köhler, Klaas J. H. Giesbertz, Frank Noé, Jan Hermann, Paola Gori-Giorgi, Adam Foster
2024	A	`conf`	Making Better Use of Unlabelled Data in Bayesian Active Learning. AISTATS Freddie Bickford Smith, Adam Foster, Tom Rainforth
2024	J	`jnl`	Making Better Use of Unlabelled Data in Bayesian Active Learning. CoRR Freddie Bickford Smith, Adam Foster, Tom Rainforth
2024	J	`jnl`	Rethinking Aleatoric and Epistemic Uncertainty. CoRR Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth
2023	A*	`conf`	CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. ICML Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
2023	J	`jnl`	CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. CoRR Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster
2023	A*	`conf`	Differentiable Multi-Target Causal Bayesian Experimental Design. ICML Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
2023	J	`jnl`	Differentiable Multi-Target Causal Bayesian Experimental Design. CoRR Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer
2023	A*	`conf`	Learning Instance-Specific Augmentations by Capturing Local Invariances. ICML Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim
2023	J	`jnl`	Modern Bayesian Experimental Design. CoRR Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith
2023	A	`conf`	Prediction-Oriented Bayesian Active Learning. AISTATS Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
2023	J	`jnl`	Prediction-Oriented Bayesian Active Learning. CoRR Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
2022	A*	`conf`	Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness. ICML Adam Foster, Árpi Vezér, Craig A. Glastonbury, Páidí Creed, Samer Abujudeh, Aaron Sim
2022	J	`jnl`	Deep End-to-end Causal Inference. CoRR Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
2022	J	`jnl`	Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation. CoRR Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster
2022	J	`jnl`	Learning Instance-Specific Data Augmentations. CoRR Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim
2022	J	`jnl`	Unbiased MLMC Stochastic Gradient-Based Optimization of Bayesian Experimental Designs. SIAM J. Sci. Comput. Takashi Goda, Tomohiko Hironaka, Wataru Kitade, Adam Foster
2021	J	`jnl`	Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness. CoRR Adam Foster, Árpi Vezér, Craig A. Glastonbury, Páidí Creed, Sam Abujudeh, Aaron Sim
2021	A*	`conf`	Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. ICML Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth
2021	J	`jnl`	Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. CoRR Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth
2021	A*	`conf`	Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. NeurIPS Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth
2021	J	`jnl`	Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. CoRR Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth
2021	A*	`conf`	Improving Transformation Invariance in Contrastive Representation Learning. ICLR Adam Foster, Rattana Pukdee, Tom Rainforth
2021	A*	`conf`	On Contrastive Representations of Stochastic Processes. NeurIPS Emile Mathieu, Adam Foster, Yee Whye Teh
2021	J	`jnl`	On Contrastive Representations of Stochastic Processes. CoRR Emile Mathieu, Adam Foster, Yee Whye Teh
2020	A	`conf`	A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. AISTATS Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
2020	J	`jnl`	Improving Transformation Invariance in Contrastive Representation Learning. CoRR Adam Foster, Rattana Pukdee, Tom Rainforth
2019	J	`jnl`	A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. CoRR Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth
2019	A*	`conf`	Variational Bayesian Optimal Experimental Design. NeurIPS Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman
2019	J	`jnl`	Variational Estimators for Bayesian Optimal Experimental Design. CoRR Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman
2018	A	`conf`	Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. UAI Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh
2018	J	`jnl`	Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. CoRR Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh