2 citations · 5 across the 9 of their papers we have counts for
9 papers
Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search
Reyan Ahmed, Mithun Ghosh, Kwang-Sung Jun +1
Graph neural networks are useful for learning problems, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem.…
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards
Hao Qin, Kwang-Sung Jun, Chicheng Zhang
We study -armed bandit problems where the reward distributions of the arms are all supported on the interval. It has been a challenge to design regret-efficient randomiz…
Tighter PAC-Bayes Bounds Through Coin-Betting
Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij +1
We consider the problem of estimating the mean of a sequence of random elements where is a fixed scalar function, ar…
Revisiting Simple Regret: Fast Rates for Returning a Good Arm
Yao Zhao, Connor James Stephens, Csaba Szepesvári +1
Simple regret is a natural and parameter-free performance criterion for pure exploration in multi-armed bandits yet is less popular than the probability of missing the best arm or…
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun
In sparse linear bandits, a learning agent sequentially selects an action and receive reward feedback, and the reward function depends linearly on a few coordinates of the covariat…
Norm-Agnostic Linear Bandits
Spencer, Gales, Sunder Sethuraman +1
Linear bandits have a wide variety of applications including recommendation systems yet they make one strong assumption: the algorithms must know an upper bound on the norm of…
An Experimental Design Approach for Regret Minimization in Logistic Bandits
Blake Mason, Kwang-Sung Jun, Lalit Jain
In this work we consider the problem of regret minimization for logistic bandits. The main challenge of logistic bandits is reducing the dependence on a potentially large problem d…
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Louis Faury, Marc Abeille, Kwang-Sung Jun +1
Logistic Bandits have recently undergone careful scrutiny by virtue of their combined theoretical and practical relevance. This research effort delivered statistically efficient al…
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits
Kwang-Sung Jun, Lalit Jain, Blake Mason +1
We propose improved fixed-design confidence bounds for the linear logistic model. Our bounds significantly improve upon the state-of-the-art bound by Li et al. (2017) via recent de…