2 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
A regret minimization approach to fixed-point iterations
Joon Kwon
We propose a conversion scheme that turns regret minimizing algorithms into fixed point iterations, with convergence guarantees following from regret bounds. The resulting iteratio…
Finite-sum optimization: Adaptivity to smoothness and loopless variance reduction
Bastien Batardière, Joon Kwon
For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster converg…
Time-Dependent Blackwell Approachability and Application to Absorbing Games
Joon Kwon, Yijun Wan, Bruno Ziliotto
Blackwell's approachability (Blackwell, 1954, 1956) is a very general online learning framework where a Decision Maker obtains vector-valued outcomes, and aims at the convergence o…
Unifying mirror descent and dual averaging
Anatoli Juditsky, Joon Kwon, Éric Moulines
We introduce and analyze a new family of first-order optimization algorithms which generalizes and unifies both mirror descent and dual averaging. Within the framework of this fami…
A continuous-time approach to online optimization
Joon Kwon, Panayotis Mertikopoulos
We consider a family of learning strategies for online optimization problems that evolve in continuous time and we show that they lead to no regret. From a more traditional, discre…
A universal bound on the variations of bounded convex functions
Joon Kwon
Given a convex set in a real vector space and two points , we investivate which are the possible values for the variation , where $f:C\longrightarrow […