| The Malmo Platform for Artificial Intelligence Experimentation. M Johnson, K Hofmann, T Hutton, D Bignell Ijcai, 4246-4247, 2016 | 396 | 2016 |
| Towards conversational recommender systems K Christakopoulou, F Radlinski, K Hofmann Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016 | 358 | 2016 |
| Fast context adaptation via meta-learning L Zintgraf, K Shiarli, V Kurin, K Hofmann, S Whiteson International Conference on Machine Learning, 7693-7702, 2019 | 338 | 2019 |
| 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 arXiv preprint arXiv:1910.08348, 2019 | 164 | 2019 |
| Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval K Hofmann, S Whiteson, M de Rijke Information Retrieval 16, 63-90, 2013 | 160 | 2013 |
| Meta reinforcement learning with latent variable gaussian processes S Sæmundsson, K Hofmann, MP Deisenroth arXiv preprint arXiv:1803.07551, 2018 | 148 | 2018 |
| A probabilistic method for inferring preferences from clicks K Hofmann, S Whiteson, M de Rijke CIKM 2011: Proceedings of the Twentieth Conference on Information and …, 2011 | 136 | 2011 |
| Reusing historical interaction data for faster online learning to rank for IR K Hofmann, A Schuth, S Whiteson, M De Rijke Proceedings of the sixth ACM international conference on Web search and data …, 2013 | 126 | 2013 |
| Generalization in reinforcement learning with selective noise injection and information bottleneck M Igl, K Ciosek, Y Li, S Tschiatschek, C Zhang, S Devlin, K Hofmann Advances in neural information processing systems 32, 2019 | 125 | 2019 |
| Online evaluation for information retrieval K Hofmann, L Li, F Radlinski Foundations and Trends® in Information Retrieval 10 (1), 1-117, 2016 | 115 | 2016 |
| Better exploration with optimistic actor critic K Ciosek, Q Vuong, R Loftin, K Hofmann Advances in Neural Information Processing Systems 32, 2019 | 112 | 2019 |
| Automatic curriculum learning for deep rl: A short survey R Portelas, C Colas, L Weng, K Hofmann, PY Oudeyer arXiv preprint arXiv:2003.04664, 2020 | 106 | 2020 |
| Teacher algorithms for curriculum learning of deep rl in continuously parameterized environments R Portelas, C Colas, K Hofmann, PY Oudeyer Conference on Robot Learning, 835-853, 2020 | 103 | 2020 |
| Generating a non-english subjectivity lexicon: Relations that matter V Jijkoun, K Hofmann Proceedings of the 12th Conference of the European Chapter of the ACL (EACL …, 2009 | 87 | 2009 |
| Balancing exploration and exploitation in learning to rank online K Hofmann, S Whiteson, M De Rijke Advances in Information Retrieval: 33rd European Conference on IR Research …, 2011 | 85 | 2011 |
| On user interactions with query auto-completion B Mitra, M Shokouhi, F Radlinski, K Hofmann Proceedings of the 37th international ACM SIGIR conference on Research …, 2014 | 78 | 2014 |
| An eye-tracking study of user interactions with query auto completion K Hofmann, B Mitra, F Radlinski, M Shokouhi Proceedings of the 23rd ACM International Conference on Conference on …, 2014 | 71 | 2014 |
| A new AI evaluation cosmos: Ready to play the game? J Hernández-Orallo, M Baroni, J Bieger, N Chmait, DL Dowe, K Hofmann, ... AI Magazine 38 (3), 66-69, 2017 | 70 | 2017 |
| Contextual dueling bandits M Dudík, K Hofmann, RE Schapire, A Slivkins, M Zoghi Conference on Learning Theory, 563-587, 2015 | 69 | 2015 |
| Contextual factors for finding similar experts K Hofmann, K Balog, T Bogers, M de Rijke Journal of the American Society for Information Science and Technology 61 (5 …, 2010 | 69 | 2010 |
Maarten de RijkeUniversity of Amsterdam & ICAIVerified email at uva.nl
