{"id":571,"date":"2020-03-04T16:21:12","date_gmt":"2020-03-04T15:21:12","guid":{"rendered":"http:\/\/project.inria.fr\/inriacwi\/?page_id=571"},"modified":"2021-09-07T16:51:58","modified_gmt":"2021-09-07T14:51:58","slug":"4tune","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/inriacwi\/4tune\/","title":{"rendered":"4TUNE"},"content":{"rendered":"

<\/p>\n

Associate team 4TUNE \u2013 Adaptive, Efficient, Provable and Flexible Tuning for Machine Learning<\/strong><\/span><\/h3>\n

Principal investigators<\/strong>
\nPierre Gaillard<\/a>, THOTH<\/a>\u00a0research\u00a0team, Inria
\nPeter Grunwald<\/a>, Machine Learning Team<\/a>, CWI<\/p>\n

Abstract<\/strong>
\nThe long-term goal of 4TUNE is to push adaptive machine learning to the next level. We aim to develop refined methods, going beyond traditional worst-case analysis, for exploiting structure in the learning problem at hand. We will develop new theory and design sophisticated algorithms for the core tasks of statistical learning and individual sequence prediction. We are especially interested in understanding the connections between these tasks and developing unified methods for both. We will also investigate adaptivity to non-standard patterns encountered in embedded learning tasks, in particular in iterative equilibrium computations.<\/p>\n

Website<\/strong>:\u00a0http:\/\/pierre.gaillard.me\/4tune\/<\/a><\/p>\n

Keywords<\/strong>: Machine Learning and statistics, Optimisation, Artificial Intelligence<\/p>","protected":false},"excerpt":{"rendered":"

Associate team 4TUNE \u2013 Adaptive, Efficient, Provable and Flexible Tuning for Machine Learning Principal investigators\u2026<\/p>\n

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