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Adam Foster
Adam Foster
Microsoft Research
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
Variational bayesian optimal experimental design
A Foster, M Jankowiak, E Bingham, P Horsfall, YW Teh, T Rainforth, ...
Advances in Neural Information Processing Systems (NeurIPS 2019), 2019
1282019
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
A Foster, DR Ivanova, I Malik, T Rainforth
38th International Conference on Machine Learning (ICML 2021), 2021
712021
Deep end-to-end causal inference
T Geffner, J Antoran, A Foster, W Gong, C Ma, E Kiciman, A Sharma, ...
arXiv preprint arXiv:2202.02195, 2022
692022
A unified stochastic gradient approach to designing bayesian-optimal experiments
A Foster, M Jankowiak, M O’Meara, YW Teh, T Rainforth
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020
622020
Modern bayesian experimental design
T Rainforth, A Foster, DR Ivanova, FB Smith
Statistical Science, 2023
412023
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
D Ivanova, A Foster, S Kleinegesse, MU Gutmann, T Rainforth
Advances in Neural Information Processing Systems (NeurIPS 2021), 2021
392021
Improving Transformation Invariance in Contrastive Representation Learning
A Foster, R Pukdee, T Rainforth
International Conference on Learning Representations (ICLR 2021), 2020
232020
Variational, Monte Carlo and policy-based approaches to Bayesian experimental design
AE Foster
University of Oxford, 2022
212022
Prediction-oriented bayesian active learning
FB Smith, A Kirsch, S Farquhar, Y Gal, A Foster, T Rainforth
International Conference on Artificial Intelligence and Statistics, 7331-7348, 2023
20*2023
Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs
T Goda, T Hironaka, W Kitade, A Foster
SIAM Journal on Scientific Computing 44 (1), A286-A311, 2022
182022
On Contrastive Representations of Stochastic Processes
E Mathieu, A Foster, YW Teh
Advances in Neural Information Processing Systems (NeurIPS 2021), 2021
152021
Sampling and inference for Beta Neutral-to-the-Left models of sparse networks
B Bloem-Reddy, A Foster, E Mathieu, YW Teh
Conference on Uncertainty in Artificial Intelligence (UAI 2018), 2018
112018
Differentiable Multi-Target Causal Bayesian Experimental Design
Y Annadani, P Tigas, DR Ivanova, A Jesson, Y Gal, A Foster, S Bauer
40th International Conference on Machine Learning (ICML 2023), 2023
10*2023
A Causal AI Suite for Decision-Making
E Kiciman, EW Dillon, D Edge, A Foster, A Hilmkil, J Jennings, C Ma, ...
NeurIPS 2022 Workshop on Causality for Real-world Impact, 2022
92022
Learning Instance-Specific Augmentations by Capturing Local Invariances
N Miao, T Rainforth, E Mathieu, Y Dubois, YW Teh, A Foster, H Kim
40th International Conference on Machine Learning (ICML 2023), 2023
8*2023
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
A Foster, Á Vezér, CA Glastonbury, P Creed, S Abujudeh, A Sim
39th International Conference on Machine Learning (ICML 2022), 2021
72021
Variational Optimal Experiment Design: Efficient Automation of Adaptive Experiments
A Foster, M Jankowiak, E Bingham, YW Teh, T Rainforth, N Goodman
Third workshop on Bayesian Deep Learning at NeurIPS 2018, 2018
52018
Instance-specific augmentation: Capturing local invariances
N Miao, T Rainforth, E Mathieu, Y Dubois, YW Teh, A Foster, H Kim
42022
Sampling and inference for discrete random probability measures in probabilistic programs
B Bloem-Reddy, E Mathieu, A Foster, T Rainforth, YW Teh, H Ge, ...
Workshop on Advances in Approximate Bayesian Inference at NeurIPS 2017, 2017
42017
An extension of standard latent Dirichlet allocation to multiple corpora
A Foster, H Li, G Maierhofer, M Shearer
SIAM Undergraduate Research Online 9, 2016
42016
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