| Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition B Thomas, S Kessler, S Karout ICASSP 2022, 2022 | 99 | 2022 |
| Continual-wav2vec2: an application of continual learning for self-supervised automatic speech recognition S Kessler, B Thomas, S Karout arXiv preprint arXiv:2107.13530, 2021 | 58* | 2021 |
| Same State, Different Task: Continual Reinforcement Learning without Interference S Kessler, J Parker-Holder, P Ball, S Zohren, SJ Roberts AAAI 2022, 2021 | 51* | 2021 |
| Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning S Kessler, V Nguyen, S Zohren, S Roberts UAI 2021, 2020 | 37* | 2020 |
| The Effectiveness of World Models for Continual Reinforcement Learning S Kessler, M Ostaszewski, MP Bortkiewicz, M Żarski, M Wolczyk, ... Conference on Lifelong Learning Agents, 184-204, 2023 | 14* | 2023 |
| Follow the Leader: Technical and Inspirational Leadership in Open Source Software J Hergueux, S Kessler Proceedings of the ACM CHI Conference on Human Factors in Computing Systems 2022, 2022 | 9 | 2022 |
| Fisher Flow Matching for Generative Modeling over Discrete Data O Davis, S Kessler, M Petrache, İ İlkan Ceylan, M Bronstein, AJ Bose NeurIPs 2024, 2024 | 8 | 2024 |
| On Sequential Bayesian Inference for Continual Learning S Kessler, A Cobb, TGJ Rudner, S Zohren, SJ Roberts Entropy 25 (6), 884, 2023 | 8* | 2023 |
| Practical Bayesian Learning of Neural Networks via Adaptive Optimisation Methods S Kessler, A Salas, VWC Tan, S Zohren, S Roberts ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning, 2018 | 8* | 2018 |
| Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies K Wood, S Kessler, SJ Roberts, S Zohren arXiv preprint arXiv:2310.10500, 2023 | 3 | 2023 |
| SAVA: Scalable Learning-Agnostic Data Valuation S Kessler, T Le, V Nguyen ICLR 2025, 2024 | | 2024 |
| On Efficient Distillation from LLMs to SLMs M Jazbec, M Xia, A Mallick, D Madrigal, D Han, S Kessler, V Rühle NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles …, 2024 | | 2024 |
| Retaining Skills Under Distribution Shifts: Sequential Bayesian Inference, Reinforcement Learning and Applications SC Kessler University of Oxford (United Kingdom), 2023 | | 2023 |
Stephen RobertsProfessor of Engineering Science (Machine Learning, Information Engineering), University of OxfordEmail verificata su robots.ox.ac.uk