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ORCIDAdam Foster 0001
Person information
- affiliation: Microsoft Research, Cambridge, UK
- affiliation (PhD 2021): University of Oxford, Department of Statistics, UK
Other persons with the same name
- Adam Foster 0002 — Birkbeck University of London, London, UK
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2020 – today
- 2024
[j2]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:
Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 2024 (2024)- [c13]Freddie Bickford Smith, Adam Foster, Tom Rainforth:
Making Better Use of Unlabelled Data in Bayesian Active Learning. AISTATS 2024: 847-855 - [i18]Freddie Bickford Smith, Adam Foster, Tom Rainforth:
Making Better Use of Unlabelled Data in Bayesian Active Learning. CoRR abs/2404.17249 (2024) - [i17]Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster:
Amortized Active Causal Induction with Deep Reinforcement Learning. CoRR abs/2405.16718 (2024) - [i16]Lixue Cheng, P. Bernát Szabó, Zeno Schätzle, Derk Kooi, Jonas Köhler, Klaas J. H. Giesbertz, Frank Noé, Jan Hermann, Paola Gori-Giorgi, Adam Foster:
Highly Accurate Real-space Electron Densities with Neural Networks. CoRR abs/2409.01306 (2024) - 2023
- [c12]Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth:
Prediction-Oriented Bayesian Active Learning. AISTATS 2023: 7331-7348 - [c11]Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. ICML 2023: 14445-14464 - [c10]Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim:
Learning Instance-Specific Augmentations by Capturing Local Invariances. ICML 2023: 24720-24736 - [c9]Panagiotis Tigas, Yashas Annadani, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer:
Differentiable Multi-Target Causal Bayesian Experimental Design. ICML 2023: 34263-34279 - [i15]Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster, Stefan Bauer:
Differentiable Multi-Target Causal Bayesian Experimental Design. CoRR abs/2302.10607 (2023) - [i14]Desi R. Ivanova, Joel Jennings, Tom Rainforth, Cheng Zhang, Adam Foster:
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design. CoRR abs/2302.14015 (2023) - [i13]Tom Rainforth, Adam Foster, Desi R. Ivanova, Freddie Bickford Smith:
Modern Bayesian Experimental Design. CoRR abs/2302.14545 (2023) - [i12]Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth:
Prediction-Oriented Bayesian Active Learning. CoRR abs/2304.08151 (2023) - 2022
- [j1]Takashi Goda
, Tomohiko Hironaka, Wataru Kitade, Adam Foster:
Unbiased MLMC Stochastic Gradient-Based Optimization of Bayesian Experimental Designs. SIAM J. Sci. Comput. 44(1): 286- (2022) - [c8]Adam Foster, Árpi Vezér, Craig A. Glastonbury, Páidí Creed, Samer Abujudeh, Aaron Sim:
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness. ICML 2022: 6578-6621 - [i11]Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang:
Deep End-to-end Causal Inference. CoRR abs/2202.02195 (2022) - [i10]Ning Miao, Emile Mathieu, Yann Dubois, Tom Rainforth, Yee Whye Teh, Adam Foster, Hyunjik Kim:
Learning Instance-Specific Data Augmentations. CoRR abs/2206.00051 (2022) - [i9]Desi R. Ivanova, Joel Jennings, Cheng Zhang, Adam Foster:
Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation. CoRR abs/2207.05250 (2022) - 2021
- [c7]Adam Foster, Rattana Pukdee, Tom Rainforth:
Improving Transformation Invariance in Contrastive Representation Learning. ICLR 2021 - [c6]Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth:
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. ICML 2021: 3384-3395 - [c5]Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Thomas Rainforth:
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. NeurIPS 2021: 25785-25798 - [c4]Emile Mathieu, Adam Foster, Yee Whye Teh:
On Contrastive Representations of Stochastic Processes. NeurIPS 2021: 28823-28835 - [i8]Adam Foster, Desi R. Ivanova, Ilyas Malik, Tom Rainforth:
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design. CoRR abs/2103.02438 (2021) - [i7]Adam Foster, Árpi Vezér, Craig A. Glastonbury, Páidí Creed, Sam Abujudeh, Aaron Sim:
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness. CoRR abs/2106.08161 (2021) - [i6]Emile Mathieu, Adam Foster, Yee Whye Teh:
On Contrastive Representations of Stochastic Processes. CoRR abs/2106.10052 (2021) - [i5]Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth:
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods. CoRR abs/2111.02329 (2021) - 2020
- [c3]Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth:
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. AISTATS 2020: 2959-2969 - [i4]Adam Foster, Rattana Pukdee, Tom Rainforth:
Improving Transformation Invariance in Contrastive Representation Learning. CoRR abs/2010.09515 (2020)
2010 – 2019
- 2019
- [c2]Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman:
Variational Bayesian Optimal Experimental Design. NeurIPS 2019: 14036-14047 - [i3]Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah D. Goodman:
Variational Estimators for Bayesian Optimal Experimental Design. CoRR abs/1903.05480 (2019) - [i2]Adam Foster, Martin Jankowiak, Matthew O'Meara, Yee Whye Teh, Tom Rainforth:
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments. CoRR abs/1911.00294 (2019) - 2018
- [c1]Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh:
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. UAI 2018: 477-486 - [i1]Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh:
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. CoRR abs/1807.03113 (2018)
Coauthor Index
aka: Thomas Rainforth
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last updated on 2024-10-24 21:30 CEST by the dblp team
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