Folgen
Katja Hofmann
Katja Hofmann
Microsoft Research
Bestätigte E-Mail-Adresse bei microsoft.com - Startseite
Titel
Zitiert von
Zitiert von
Jahr
The Malmo Platform for Artificial Intelligence Experimentation.
M Johnson, K Hofmann, T Hutton, D Bignell
Ijcai, 4246-4247, 2016
4122016
Towards conversational recommender systems
K Christakopoulou, F Radlinski, K Hofmann
Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016
3842016
Fast context adaptation via meta-learning
L Zintgraf, K Shiarli, V Kurin, K Hofmann, S Whiteson
International Conference on Machine Learning, 7693-7702, 2019
3432019
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
1842019
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
1612013
Meta reinforcement learning with latent variable gaussian processes
S Sæmundsson, K Hofmann, MP Deisenroth
arXiv preprint arXiv:1803.07551, 2018
1582018
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
1402019
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
1372011
Better exploration with optimistic actor critic
K Ciosek, Q Vuong, R Loftin, K Hofmann
Advances in Neural Information Processing Systems 32, 2019
1272019
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
1272013
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
1242020
Online evaluation for information retrieval
K Hofmann, L Li, F Radlinski
Foundations and Trends® in Information Retrieval 10 (1), 1-117, 2016
1232016
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
1192020
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
882009
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
852011
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
792014
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
732017
Contextual dueling bandits
M Dudík, K Hofmann, RE Schapire, A Slivkins, M Zoghi
Conference on Learning Theory, 563-587, 2015
732015
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
722014
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
722010
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20