48 citations · 147 across the 15 of their papers we have counts for
16 papers
Uniform Race: Parameter-Free Approximate Rejection Sampling
Seiyun Shin, Juhyeong Pang, Kwang-Sung Jun
We study approximate sampling: given independent samples from a proposal distribution , the goal is to select one whose distribution is close to a target specified only…
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…
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality
Kwang-Sung Jun, Chicheng Zhang
We study stochastic structured bandits for minimizing regret. The fact that the popular optimistic algorithms do not achieve the asymptotic instance-dependent regret optimality (as…
Parameter-Free Locally Differentially Private Stochastic Subgradient Descent
Kwang-Sung Jun, Francesco Orabona
We consider the problem of minimizing a convex risk with stochastic subgradients guaranteeing -locally differentially private (-LDP). While it has been shown that stochastic…
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
In this paper, we consider the nonparametric least square regression in a Reproducing Kernel Hilbert Space (RKHS). We propose a new randomized algorithm that has optimal generaliza…
Parameter-Free Online Convex Optimization with Sub-Exponential Noise
Kwang-Sung Jun, Francesco Orabona
We consider the problem of unconstrained online convex optimization (OCO) with sub-exponential noise, a strictly more general problem than the standard OCO. In this setting, the le…
Bilinear Bandits with Low-rank Structure
Kwang-Sung Jun, Rebecca Willett, Stephen Wright +1
We introduce the bilinear bandit problem with low-rank structure in which an action takes the form of a pair of arms from two different entity types, and the reward is a bilinear f…
Adversarial Attacks on Stochastic Bandits
Kwang-Sung Jun, Lihong Li, Yuzhe Ma +1
We study adversarial attacks that manipulate the reward signals to control the actions chosen by a stochastic multi-armed bandit algorithm. We propose the first attack against two…
Data Poisoning Attacks in Contextual Bandits
Yuzhe Ma, Kwang-Sung Jun, Lihong Li +1
We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical…
Online Learning for Changing Environments using Coin Betting
Kwang-Sung Jun, Francesco Orabona, Stephen Wright +1
A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments. Recent studies have proposed "meta" algorithms that convert any onlin…
Scalable Generalized Linear Bandits: Online Computation and Hashing
Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak +1
Generalized Linear Bandits (GLBs), a natural extension of the stochastic linear bandits, has been popular and successful in recent years. However, existing GLBs scale poorly with t…
Improved Strongly Adaptive Online Learning using Coin Betting
Kwang-Sung Jun, Francesco Orabona, Rebecca Willett +1
This paper describes a new parameter-free online learning algorithm for changing environments. In comparing against algorithms with the same time complexity as ours, we obtain a st…
Graph-Based Active Learning: A New Look at Expected Error Minimization
Kwang-Sung Jun, Robert Nowak
In graph-based active learning, algorithms based on expected error minimization (EEM) have been popular and yield good empirical performance. The exact computation of EEM optimally…