| Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning A Kirsch, J van Amersfoort, Y Gal Advances in Neural Information Processing Systems (NeurIPS), 7024-7035, 2019 | 732 | 2019 |
| Deep Deterministic Uncertainty: A New Simple Baseline J Mukhoti, A Kirsch, J van Amersfoort, PHS Torr, Y Gal Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 276* | 2023 |
| Prioritized training on points that are learnable, worth learning, and not yet learnt S Mindermann, JM Brauner, MT Razzak, M Sharma, A Kirsch, W Xu, ... International Conference on Machine Learning (ICML), 15630-15649, 2022 | 162 | 2022 |
| Plex: towards reliability using pretrained large model extensions (2022) D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ... URL https://arxiv. org/abs/2207.07411, 0 | 134* | |
| Prediction-Oriented Bayesian Active Learning F Bickford Smith, A Kirsch, S Farquhar, Y Gal, A Foster, T Rainforth International Conference on Artificial Intelligence and Statistics (AISTATS …, 2023 | 64* | 2023 |
| Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning A Kirsch, S Farquhar, P Atighehchian, A Jesson, F Branchaud-Charron, ... Transactions on Machine Learning Research (TMLR), 2023 | 53* | 2023 |
| Causal-bald: Deep bayesian active learning of outcomes to infer treatment-effects from observational data A Jesson, P Tigas, J van Amersfoort, A Kirsch, U Shalit, Y Gal Advances in Neural Information Processing Systems (NeurIPS) 34, 30465-30478, 2021 | 35 | 2021 |
| Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities A Kirsch, Y Gal Transactions on Machine Learning Research (TMLR), 2022 | 26 | 2022 |
| Unpacking information bottlenecks: Unifying information-theoretic objectives in deep learning A Kirsch, C Lyle, Y Gal Workshop Uncertainty & Robustness in Deep Learning at Int. Conf. on Machine …, 2020 | 23* | 2020 |
| A Note on "Assessing Generalization of SGD via Disagreement" A Kirsch, Y Gal Transactions on Machine Learning Research (TMLR), 2022 | 21 | 2022 |
| Black-Box Batch Active Learning for Regression A Kirsch Transactions on Machine Learning Research, 2023 | 9 | 2023 |
| Turning up the heat: Min-p sampling for creative and coherent llm outputs M Nguyen, A Baker, C Neo, A Roush, A Kirsch, R Shwartz-Ziv arXiv preprint arXiv:2407.01082, 2024 | 7* | 2024 |
| Does Deep Learning on a Data Diet reproduce? Overall yes, but GraNd at Initialization does not A Kirsch Transactions on Machine Learning Research, 2023 | 7 | 2023 |
| Color-filter: Conditional loss reduction filtering for targeted language model pre-training D Brandfonbrener, H Zhang, A Kirsch, JR Schwarz, S Kakade Advances in Neural Information Processing Systems 37, 97618-97649, 2024 | 5 | 2024 |
| Advancing deep active learning & data subset selection: Unifying principles with information-theory intuitions A Kirsch arXiv preprint arXiv:2401.04305, 2024 | 5 | 2024 |
| A Practical & Unified Notation for Information-Theoretic Quantities in ML A Kirsch, Y Gal arXiv preprint arXiv:2106.12062, 2021 | 5 | 2021 |
| Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning A Kirsch arXiv preprint arXiv:2301.09490, 2023 | 2 | 2023 |
| Marginal and joint cross-entropies & predictives for online Bayesian inference, active learning, and active sampling A Kirsch, J Kossen, Y Gal arXiv preprint arXiv:2205.08766, 2022 | 2 | 2022 |
| MDP environments for the OpenAI Gym A Kirsch arXiv preprint arXiv:1709.09069, 2017 | 2 | 2017 |
| All models are wrong, some are useful: Model Selection with Limited Labels P Okanovic, A Kirsch, J Kasper, T Hoefler, A Krause, NM Gürel arXiv preprint arXiv:2410.13609, 2024 | 1 | 2024 |
Yarin GalAssociate Professor, University of OxfordE-mail confirmado em cs.ox.ac.uk