| 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 | 164 | 2019 |
| Modern bayesian experimental design T Rainforth, A Foster, DR Ivanova, FB Smith Statistical Science, 2023 | 104 | 2023 |
| 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 | 100 | 2021 |
| Deep end-to-end causal inference T Geffner, J Antoran, A Foster, W Gong, C Ma, E Kiciman, A Sharma, ... Transactions on Machine Learning Research, 2022 | 87 | 2022 |
| 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 | 81 | 2020 |
| 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 | 63 | 2021 |
| 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 | 44* | 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 | 27 | 2022 |
| Variational, Monte Carlo and policy-based approaches to Bayesian experimental design AE Foster University of Oxford, 2022 | 26 | 2022 |
| Improving Transformation Invariance in Contrastive Representation Learning A Foster, R Pukdee, T Rainforth International Conference on Learning Representations (ICLR 2021), 2020 | 25 | 2020 |
| On Contrastive Representations of Stochastic Processes E Mathieu, A Foster, YW Teh Advances in Neural Information Processing Systems (NeurIPS 2021), 2021 | 18 | 2021 |
| 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 | 17* | 2023 |
| 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 | 13 | 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 | 11 | 2022 |
| 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 | 10 | 2018 |
| 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 | 7 | 2021 |
| 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 | 5 | 2018 |
| An extension of standard latent Dirichlet allocation to multiple corpora A Foster, H Li, G Maierhofer, M Shearer SIAM Undergraduate Research Online 9, 2016 | 5 | 2016 |
| CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design DR Ivanova, J Jennings, T Rainforth, C Zhang, A Foster 40th International Conference on Machine Learning (ICML 2023), 2023 | 4 | 2023 |
| Instance-specific augmentation: Capturing local invariances N Miao, T Rainforth, E Mathieu, Y Dubois, YW Teh, A Foster, H Kim | 4 | 2022 |
Tom RainforthAssociate Professor, University of OxfordE-mail confirmado em stats.ox.ac.uk
