48 citations · 147 across the 15 of their papers we have counts for
4 papers · 1 filter
Maillard Sampling: Boltzmann Exploration Done Optimally
Jie Bian, Kwang-Sung Jun
The PhD thesis of Maillard (2013) presents a rather obscure algorithm for the -armed bandit problem. This less-known algorithm, which we call Maillard sampling (MS), computes th…
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs
Yeoneung Kim, Insoon Yang, Kwang-Sung Jun
In online learning problems, exploiting low variance plays an important role in obtaining tight performance guarantees yet is challenging because variances are often not known a pr…
Tight Concentrations and Confidence Sequences from the Regret of Universal Portfolio
Francesco Orabona, Kwang-Sung Jun
A classic problem in statistics is the estimation of the expectation of random variables from samples. This gives rise to the tightly connected problems of deriving concentration i…
Transfer Learning in Bandits with Latent Continuity
Hyejin Park, Seiyun Shin, Kwang-Sung Jun +1
Structured stochastic multi-armed bandits provide accelerated regret rates over the standard unstructured bandit problems. Most structured bandits, however, assume the knowledge of…