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Francesco Orabona
@bremen79
Dad and associate professor at @KAUST_News. Formerly @BU_ece, @sbucompsc, @YahooResearch, @TTIC_Connect. ML theory&practice, obsessed with history of science
参加 February 2010
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Our COLT 2026 paper! A single stepsize with high probability gives both fast and robust rates in TD learning, without using any projection. IMO, Wei-Cheng put together a very interesting proof, with some new tricks as well.
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1/11 New paper: A Single Stepsize Suffices for Unprojected Linear TD(0) Can TD(0) without projection and knowledge of curvature achieve high-probability rates that are both robust and fast under Markovian sampling? We show: one curvature-free stepsize + PR average suffice. 🧵
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