| Visualizing deep neural network decisions: Prediction difference analysis LM Zintgraf, TS Cohen, T Adel, M Welling The fifth International Conference on Learning Representations (ICLR 2017), 2017 | 705 | 2017 |
| Fast Context Adaptation via Meta-Learning LM Zintgraf, K Shiarlis, V Kurin, K Hofmann, S Whiteson Thirty-sixth International Conference on Machine Learning (ICML 2019), 2018 | 307 | 2018 |
| Deep Variational Reinforcement Learning for POMDPs M Igl, L Zintgraf, TA Le, F Wood, S Whiteson Thirty-fifth International Conference on Machine Learning (ICML 2018), 2018 | 218 | 2018 |
| VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning L Zintgraf, K Shiarlis, M Igl, S Schulze, Y Gal, K Hofmann, S Whiteson International Conference on Learning Representations (ICLR) 2020, 2019 | 139 | 2019 |
| A practical guide to multi-objective reinforcement learning and planning CF Hayes, R Rădulescu, E Bargiacchi, J Källström, M Macfarlane, ... Autonomous Agents and Multi-Agent Systems 36 (1), 26, 2022 | 65 | 2022 |
| Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making LM Zintgraf, DM Roijers, S Linders, CM Jonker, A Nowé 17th International Conference on Autonomous Agents and Multiagent Systems …, 2018 | 47 | 2018 |
| Interpretation of microbiota-based diagnostics by explaining individual classifier decisions A Eck, LM Zintgraf, EFJ de Groot, TGJ de Meij, TS Cohen, PHM Savelkoul, ... BMC bioinformatics 18 (1), 1-13, 2017 | 27 | 2017 |
| Interactive thompson sampling for multi-objective multi-armed bandits DM Roijers, LM Zintgraf, A Nowé Algorithmic Decision Theory: 5th International Conference, ADT 2017 …, 2017 | 25 | 2017 |
| Quality assessment of MORL algorithms: A utility-based approach LM Zintgraf, TV Kanters, DM Roijers, F Oliehoek, P Beau Benelearn 2015: proceedings of the 24th annual machine learning conference …, 2015 | 25 | 2015 |
| Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning L Zintgraf, L Feng, C Lu, M Igl, K Hartikainen, K Hofmann, S Whiteson International Conference on Machine Learning (ICML) 2021, 2020 | 22 | 2020 |
| Orbit: A real-world few-shot dataset for teachable object recognition D Massiceti, L Zintgraf, J Bronskill, L Theodorou, MT Harris, E Cutrell, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 19 | 2021 |
| Visualizing deep neural network decisions: Prediction difference analysis, arXiv LM Zintgraf, TS Cohen, T Adel, M Welling arXiv preprint arXiv:1702.04595 1702, 2017 | 16 | 2017 |
| Deep Interactive Bayesian Reinforcement Learning via Meta-Learning L Zintgraf, S Devlin, K Ciosek, S Whiteson, K Hofmann AAMAS 2021 (Extended Abstract), 2021 | 14 | 2021 |
| Interactive multi-objective reinforcement learning in multi-armed bandits for any utility function DM Roijers, LM Zintgraf, P Libin, A Nowé ALA workshop at FAIM 8, 2018 | 12 | 2018 |
| Disability-first dataset creation: Lessons from constructing a dataset for teachable object recognition with blind and low vision data collectors L Theodorou, D Massiceti, L Zintgraf, S Stumpf, C Morrison, E Cutrell, ... Proceedings of the 23rd International ACM SIGACCESS Conference on Computers …, 2021 | 10 | 2021 |
| MORL-Glue: A benchmark suite for multi-objective reinforcement learning P Vamplew, D Webb, LM Zintgraf, DM Roijers, R Dazeley, R Issabekov, ... 29th Benelux Conference on Artificial Intelligence November 8–9, 2017 …, 2017 | 9 | 2017 |
| Variational Task Embeddings for Fast Adaptation in Deep Reinforcement Learning L Zintgraf, M Igl, K Shiarlis, A Mahajan, K Hofmann, S Whiteson | 9* | |
| VariBAD: variational Bayes-adaptive deep RL via meta-learning L Zintgraf, S Schulze, C Lu, L Feng, M Igl, K Shiarlis, Y Gal, K Hofmann, ... The Journal of Machine Learning Research 22 (1), 13198-13236, 2021 | 8 | 2021 |
| Prospect pruning: Finding trainable weights at initialization using meta-gradients M Alizadeh, SA Tailor, LM Zintgraf, J van Amersfoort, S Farquhar, ... arXiv preprint arXiv:2202.08132, 2022 | 7 | 2022 |
| On the practical consistency of meta-reinforcement learning algorithms Z Xiong, L Zintgraf, J Beck, R Vuorio, S Whiteson arXiv preprint arXiv:2112.00478, 2021 | 5 | 2021 |
Shimon WhitesonProfessor of Computer Science, University of Oxford / Head of Research, Waymo UKEmail verificata su cs.ox.ac.uk
