no code implementations • 9 Mar 2023 • Joon Kwon, Bruno Ziliotto
Blackwell's approachability is a very general sequential decision framework where a Decision Maker obtains vector-valued outcomes, and aims at the convergence of the average outcome to a given "target" set.
no code implementations • 8 Sep 2020 • Joon Kwon
Blackwell's approachability is a framework where two players, the Decision Maker and the Environment, play a repeated game with vector-valued payoffs.
no code implementations • 30 Oct 2019 • 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.
no code implementations • 5 Jun 2017 • Joon Kwon, Vianney Perchet, Claire Vernade
In the classical multi-armed bandit problem, d arms are available to the decision maker who pulls them sequentially in order to maximize his cumulative reward.
no code implementations • 26 Nov 2015 • Joon Kwon, Vianney Perchet
We demonstrate that, in the classical non-stochastic regret minimization problem with $d$ decisions, gains and losses to be respectively maximized or minimized are fundamentally different.
no code implementations • 27 Jan 2014 • 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.
