| An optimization-centric view on Bayes' rule: Reviewing and generalizing variational inference J Knoblauch, J Jewson, T Damoulas Journal of Machine Learning Research 23 (132), 1-109, 2022 | 203* | 2022 |
| Optimal continual learning has perfect memory and is np-hard J Knoblauch, H Husain, T Diethe International Conference on Machine Learning, 5327-5337, 2020 | 103 | 2020 |
| Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences J Knoblauch, JE Jewson, T Damoulas Advances in Neural Information Processing Systems 31, 2018 | 76 | 2018 |
| Robust generalised Bayesian inference for intractable likelihoods T Matsubara, J Knoblauch, FX Briol, CJ Oates Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022 | 64 | 2022 |
| Spatio-temporal Bayesian on-line changepoint detection with model selection J Knoblauch, T Damoulas International Conference on Machine Learning, 2718-2727, 2018 | 61 | 2018 |
| Transforming Gaussian processes with normalizing flows J Maroñas, O Hamelijnck, J Knoblauch, T Damoulas International Conference on Artificial Intelligence and Statistics, 1081-1089, 2021 | 33 | 2021 |
| Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap C Dellaporta, J Knoblauch, T Damoulas, FX Briol International Conference on Artificial Intelligence and Statistics, 943-970, 2022 | 28 | 2022 |
| Uncertainty-aware deep learning methods for robust diabetic retinopathy classification J Jaskari, J Sahlsten, T Damoulas, J Knoblauch, S Särkkä, L Kärkkäinen, ... IEEE Access 10, 76669-76681, 2022 | 26 | 2022 |
| Generalized posteriors in approximate Bayesian computation SM Schmon, PW Cannon, J Knoblauch arXiv preprint arXiv:2011.08644, 2020 | 24 | 2020 |
| Robust Deep Gaussian Processes J Knoblauch arXiv preprint arXiv:1904.02303, 2019 | 17 | 2019 |
| Generalized Bayesian Inference for Discrete Intractable Likelihood T Matsubara, J Knoblauch, FX Briol, CJ Oates Journal of the American Statistical Association, 1-11, 2023 | 12 | 2023 |
| Robust and scalable bayesian online changepoint detection M Altamirano, FX Briol, J Knoblauch International Conference on Machine Learning, 642-663, 2023 | 10 | 2023 |
| Frequentist consistency of generalized variational inference J Knoblauch arXiv preprint arXiv:1912.04946, 2019 | 10 | 2019 |
| Adversarial interpretation of Bayesian inference H Husain, J Knoblauch International Conference on Algorithmic Learning Theory, 553-572, 2022 | 8 | 2022 |
| Robust Bayesian inference for discrete outcomes with the total variation distance J Knoblauch, L Vomfell arXiv preprint arXiv:2010.13456, 2020 | 8 | 2020 |
| A rigorous link between deep ensembles and (variational) bayesian methods VD Wild, S Ghalebikesabi, D Sejdinovic, J Knoblauch Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| Robustifying likelihoods by optimistically re-weighting data M Dewaskar, C Tosh, J Knoblauch, DB Dunson arXiv preprint arXiv:2303.10525, 2023 | 4 | 2023 |
| Robust and Conjugate Gaussian Process Regression M Altamirano, FX Briol, J Knoblauch arXiv preprint arXiv:2311.00463, 2023 | 2 | 2023 |
| Outlier-robust Kalman Filtering through Generalised Bayes G Duran-Martin, M Altamirano, AY Shestopaloff, L Sánchez-Betancourt, ... arXiv preprint arXiv:2405.05646, 2024 | 1 | 2024 |
| Optimization-centric generalizations of Bayesian inference J Knoblauch University of Warwick, 2021 | 1 | 2021 |
Theodoros (Theo) DamoulasProfessor, University of Warwick - UKRI Turing AI Fellow在 warwick.ac.uk 的电子邮件经过验证
