https://peertube.r2.enst.fr/videos/watch/dcd63cfb-c7f9-43d8-a1a5-e4609c8bdd36 Speaker: Lisheng Sun, PhD student at LRI, University Paris Sud Abstract: 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. See others videos of the MLAI workshop on www.mlai-workshop.org Sat, 25 Jul 2026 23:27:16 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://peertube.r2.enst.fr https://peertube.r2.enst.fr/lazy-static/avatars/216a33fc-1ec0-4e00-aae8-030127d397f2.png https://peertube.r2.enst.fr/videos/watch/dcd63cfb-c7f9-43d8-a1a5-e4609c8bdd36 All rights reserved, unless otherwise specified in the terms specified at https://peertube.r2.enst.fr/about and potential licenses granted by each content's rightholder.