1 citations · 2 across the 11 of their papers we have counts for
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Structure Matters: Dynamic Policy Gradient
Sara Klein, Xiangyuan Zhang, Tamer Başar +2
In this work, we study -discounted infinite-horizon tabular Markov decision processes (MDPs) and introduce a framework called dynamic policy gradient (DynPG). The framework dire…
Polyak's Heavy Ball Method Achieves Accelerated Local Rate of Convergence under Polyak-Lojasiewicz Inequality
Sebastian Kassing, Simon Weissmann
In this work, we analyze the convergence of Polyak's heavy ball method in both continuous and discrete time for non-convex -objective functions satisfying the Polyak-Lojasiewi…
On the mean field limit of consensus based methods
Marvin Koß, Simon Weissmann, Jakob Zech
Consensus based optimization (CBO) employs a swarm of particles evolving as a system of stochastic differential equations (SDEs). Recently, it has been adapted to yield a derivativ…
Almost sure convergence rates of stochastic gradient methods under gradient domination
Simon Weissmann, Sara Klein, Waïss Azizian +1
Stochastic gradient methods are among the most important algorithms in training machine learning problems. While classical assumptions such as strong convexity allow a simple analy…
On the mean-field limit for Stein variational gradient descent: stability and multilevel approximation
Simon Weissmann, Jakob Zech
In this paper we propose and analyze a novel multilevel version of Stein variational gradient descent (SVGD). SVGD is a recent particle based variational inference method. For Baye…