Thursday, 3rd of October14h30 (room R2014, 660 building) (see location)
Meta-learning as a Markov Decision Process (MDP)Machine Learning (ML) has enjoyed huge successes in recent years and an ever-growing number of real-world applications rely on it. However, designing promising algorithms for a specific problem still requires huge human effort. Automated Machine Learning (AutoML) aims at taking the human out of the loop and develop machines that generate / recommend good algorithms for a given ML tasks. AutoML is usually treated as a algorithm / hyper-parameter selection problems, existing approaches include Bayesian optimization, evolutionary algorithms as well as reinforcement learning. Among them, auto-sklearn which incorporates meta-learning techniques in their search initialization, ranks consistently well in AutoML challenges. This observation oriented my research to the Meta-Learning domain, leading to my recent paper where active learning and collaborative filtering are used to assign as quickly as possible a good algorithm to a new dataset, based on a meta-learning performance matrix S, i.e. a matrix of scores of algorithms on given datasets or tasks. This direction led me to develop a novel framework based on Markov Decision Processes (MDP) and reinforcement learning (RL), which will be the main topic of this speech.
Contact: guillaume.charpiat at inria.fr
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