I am a researcher at the Machine Learning and Perception group at Microsoft Research Cambridge. I work on online evaluation and online learning to rank for information retrieval. This means that I try to understand how we can apply machine learning to directly adapt to users, by trying out new solutions and interpreting users' feedback as a noisy reinforcement signal.
Before joining Microsoft Research, I completed my PhD in the ILPS group at the University of Amsterdam, under the supervision of Maarten de Rijke and Shimon Whiteson. My thesis on Fast and Reliable Online Learning to Rank in Information Retrieval can be downloaded from my personal homepage.
- Weinan Zhang, Ulrich Paquet, and Katja Hofmann, Collective Noise Contrastive Estimation for Policy Transfer Learning, in Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016), AAAI - Association for the Advancement of Artificial Intelligence, February 2016.
- Anne Schuth, Katja Hofmann, and Filip Radlinski, Predicting Search Satisfaction Metrics with Interleaved Comparisons, in Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR), ACM – Association for Computing Machinery, August 2015.
- Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, and Masrour Zoghi, Contextual Dueling Bandits, in Proceedings of The 28th Conference on Learning Theory (COLT), July 2015.
- Yiwei Chen and Katja Hofmann, Online Learning to Rank: Absolute vs. Relative, in Proceedings of the 24th international conference on World Wide Web (WWW), ACM – Association for Computing Machinery, 20 May 2015.
- Katja Hofmann, Bhaskar Mitra, Filip Radlinski, and Milad Shokouhi, An Eye-tracking Study of User Interactions with Query Auto Completion, in Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM), ACM – Association for Computing Machinery, November 2014.
- Bhaskar Mitra, Milad Shokouhi, Filip Radlinski, and Katja Hofmann, On User Interactions with Query Auto-Completion, in Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR), ACM, July 2014.
- Katja Hofmann, Anne Schuth, Alejandro Bellogin, and Maarten de Rijke, Effects of Position Bias on Click-Based Recommender Evaluation, in 36th European Conference on Information Retrieval (ECIR'14), Springer, 2014.
- A. Schuth, K. Hofmann, S. Whiteson, and M. de Rijke, Lerot: An online learning to rank framework, in Living Lab'13: Workshop on Living Labs for Information Retrieval Evaluation, ACM, 2013.