Fri, 13 May, 12:00 - 12:45 UTC
In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from signal processing and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This presentation aims to provide a comprehensive review of top recent developments in multiple signal processing applications of the multi-armed bandit. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.
Biography
Dr. Djallel Bouneffouf has worked for many years in the field of online machine learning, with a main research interest is in building autonomous system that can learn to be competent in uncertain environments. Dr. Djallel Bouneffouf conducted his research in both public and privet sector. He spent 5 years at Nomalys a Mobile App Development Company (Paris, France) where he developed the first risk-aware recommender system, one year at Orange labs (Lannion, France) where he proposed to model the Active learning as a contextual bandit, 2 years at the BC Cancer Agency (Vancouver, Canada) where he come up with a very fast clustering algorithm helping the biologist analyzing all the collected and unstructured data and during his 5 years at IBM (USA and Ireland) he proposed the first reinforcement learning model that can mimic a different brain disorder and proposed a novel model of attention based on multi-armed bandit algorithm. According to google scholar Dr. Djallel Bouneffouf is the 10 most cited scientist in his field, he has more than 40 publications published in top tiers conferences, has over 1564 citations, and has served as a PC in more than 20 conferences.