| The Malmo Platform for Artificial Intelligence Experimentation. M Johnson, K Hofmann, T Hutton, D Bignell IJCAI, 4246-4247, 2016 | 210 | 2016 |
| 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 (1), 63-90, 2013 | 119 | 2013 |
| Towards conversational recommender systems K Christakopoulou, F Radlinski, K Hofmann Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016 | 117 | 2016 |
| 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 | 108 | 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 | 88 | 2013 |
| 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 | 77 | 2009 |
| Balancing exploration and exploitation in learning to rank online K Hofmann, S Whiteson, M De Rijke European Conference on Information Retrieval, 251-263, 2011 | 70 | 2011 |
| 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 | 62 | 2010 |
| Online evaluation for information retrieval K Hofmann, L Li, F Radlinski Foundations and Trends in Information Retrieval 10 (1), 1-117, 2016 | 55 | 2016 |
| 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 | 54 | 2014 |
| Fast context adaptation via meta-learning L Zintgraf, K Shiarli, V Kurin, K Hofmann, S Whiteson International Conference on Machine Learning, 7693-7702, 2019 | 52* | 2019 |
| Meta reinforcement learning with latent variable gaussian processes S Sæmundsson, K Hofmann, MP Deisenroth arXiv preprint arXiv:1803.07551, 2018 | 52 | 2018 |
| 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 | 48 | 2017 |
| Lerot: An online learning to rank framework A Schuth, K Hofmann, S Whiteson, M de Rijke Proceedings of the 2013 workshop on Living labs for information retrieval …, 2013 | 47 | 2013 |
| Fidelity, soundness, and efficiency of interleaved comparison methods K Hofmann, S Whiteson, MD Rijke ACM Transactions on Information Systems (TOIS) 31 (4), 1-43, 2013 | 46 | 2013 |
| 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 | 45 | 2014 |
| The Cornetto database: Architecture and user-scenarios P Vossen, K Hofmann, M de Rijke, ETK Sang, K Deschacht Proceedings of 7th Dutch-Belgian Information Retrieval Workshop DIR2007, 89-96, 2007 | 45 | 2007 |
| Contextual dueling bandits M Dudík, K Hofmann, RE Schapire, A Slivkins, M Zoghi arXiv preprint arXiv:1502.06362, 2015 | 41 | 2015 |
| Fast and reliable online learning to rank for information retrieval K Hofmann SIGIR Forum 47 (2), 140, 2013 | 34 | 2013 |
| Fast and Reliable Online Learning to Rank for Information Retrieval K Hofmann University of Amsterdam, 2013 | 34 | 2013 |
Maarten de RijkeUniversity of Amsterdam & Ahold DelhaizeEmail verificata su uva.nl
