Obserwuj
Katja Hofmann
Katja Hofmann
Microsoft Research
Zweryfikowany adres z microsoft.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
The Malmo Platform for Artificial Intelligence Experimentation.
M Johnson, K Hofmann, T Hutton, D Bignell
Ijcai 16, 4246-4247, 2016
5132016
Towards conversational recommender systems
K Christakopoulou, F Radlinski, K Hofmann
Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016
4972016
Fast context adaptation via meta-learning
L Zintgraf, K Shiarli, V Kurin, K Hofmann, S Whiteson
International Conference on Machine Learning, 7693-7702, 2019
4442019
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
2892019
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
1982020
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
1952019
Meta reinforcement learning with latent variable gaussian processes
S Sæmundsson, K Hofmann, MP Deisenroth
arXiv preprint arXiv:1803.07551, 2018
1812018
Better exploration with optimistic actor critic
K Ciosek, Q Vuong, R Loftin, K Hofmann
Advances in Neural Information Processing Systems 32, 2019
1722019
Imitating human behaviour with diffusion models
T Pearce, T Rashid, A Kanervisto, D Bignell, M Sun, R Georgescu, ...
arXiv preprint arXiv:2301.10677, 2023
1642023
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
1642013
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
1602020
Online evaluation for information retrieval
K Hofmann, L Li, F Radlinski
Foundations and Trends® in Information Retrieval 10 (1), 1-117, 2016
1442016
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
1412011
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
1372013
Contextual dueling bandits
M Dudík, K Hofmann, RE Schapire, A Slivkins, M Zoghi
Conference on Learning Theory, 563-587, 2015
1222015
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
922009
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
862011
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
842014
Variational integrator networks for physically structured embeddings
S Saemundsson, A Terenin, K Hofmann, M Deisenroth
International Conference on Artificial Intelligence and Statistics, 3078-3087, 2020
792020
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
782017
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