114 citations · 381 across the 40 of their papers we have counts for
5 papers · 1 filter
Adaptive Ensemble Learning with Confidence Bounds
Cem Tekin, Jinsung Yoon, Mihaela van der Schaar
Extracting actionable intelligence from distributed, heterogeneous, correlated and high-dimensional data sources requires run-time processing and learning both locally and globally…
Episodic Multi-armed Bandits
Cem Tekin, Mihaela van der Schaar
We introduce a new class of reinforcement learning methods referred to as {\em episodic multi-armed bandits} (eMAB). In eMAB the learner proceeds in {\em episodes}, each composed o…
Contextual Online Learning for Multimedia Content Aggregation
Cem Tekin, Mihaela van der Schaar
The last decade has witnessed a tremendous growth in the volume as well as the diversity of multimedia content generated by a multitude of sources (news agencies, social media, etc…
Global Bandits
Onur Atan, Cem Tekin, Mihaela van der Schaar
Multi-armed bandits (MAB) model sequential decision making problems, in which a learner sequentially chooses arms with unknown reward distributions in order to maximize its cumulat…
RELEAF: An Algorithm for Learning and Exploiting Relevance
Cem Tekin, Mihaela van der Schaar
Recommender systems, medical diagnosis, network security, etc., require on-going learning and decision-making in real time. These -- and many others -- represent perfect examples o…