{"id":11264,"date":"2021-10-22T19:38:43","date_gmt":"2021-10-22T17:38:43","guid":{"rendered":"https:\/\/kinit.sk\/member\/matej-cief\/"},"modified":"2025-06-25T22:54:10","modified_gmt":"2025-06-25T20:54:10","slug":"matej-cief","status":"publish","type":"member","link":"https:\/\/kinit.sk\/sk\/clen\/matej-cief\/","title":{"rendered":"Matej \u010cief"},"content":{"rendered":"
\n

Matej is a PhD student focused on recommender systems (off-policy learning and evaluation). In his research he designs estimators and data-gathering policies that can evaluate recommendation algorithms without the need of deploying the whole system online. Supervised by Michal Kompan (KInIT) and Branislav Kveton (Amazon\u2019s lab in Berkeley).<\/p>\n<\/div>\n\n

\n

He holds a Master\u2019s degree in Intelligent Software Systems from the Slovak University of Technology. During his studies, he has worked on multiple research projects, including User Modeling and his master\u2019s thesis: Use of Semi-supervised Learning for Fake News Detection. Matej has gained experience in applied machine learning research through his work as a data scientist, where he mainly focused on solving problems in the fields of natural language processing and time series forecasting.<\/p>\n<\/div>\n\n

\n

PhD topic: Recommender and adaptive web-based systems<\/h3>\n<\/div>\n\n
\n

Supervising team:<\/strong> Michal Kompan<\/a> (KInIT), Branislav Kveton<\/a> (Amazon\u2019s lab in Berkeley)<\/p>\n<\/div>\n\n

\n

Off-policy learning and evaluation for contextual multi-armed bandits are a highly desirable solution for improving the performance of recommender systems as the process can be run offline<\/em>, that is without directly interacting with the users, preventing the deployment of sub-optimal policies. Despite the many benefits of off-policy learning and evaluation, the accuracy of the policy evaluation and computational complexity of the optimization over the policy space prevent the wider use of this approach in practice. Off-policy learning and evaluation struggle the most when the action space is large and the number of available interactions is limited. In this work, we focus to address three problems: <\/p>\n<\/div>\n\n

\n

(1) Off-policy evaluation with large action spaces by proposing a novel approach that learns action embeddings, resulting in a provably better bias-variance trade-off. <\/p>\n<\/div>\n\n

\n

(2) Applying pessimistic value estimation to address the overconfidence in off-policy learning for combinatorial action spaces, while keeping the optimization computationally tractable. <\/p>\n<\/div>\n\n

\n

(3) Bridging the gap between offline and online learning by studying the safe optimal design of data-gathering policies for learning to rank problems.<\/p>\n<\/div>\n\n

\n
\n