Cognitive Models of Test-Item Effects in Human Category Learning. - Xiaojin Zhu 001; Bryan R. Gibson; Kwang-Sung Jun; Timothy T. Rogers; Joseph Harrison; Chuck Kalish (ICML, 2010)

Learning from Bullying Traces in Social Media. - Jun-Ming Xu 002; Kwang-Sung Jun; Xiaojin Zhu 001; Amy Bellmore (HLT-NAACL, 2012)

Learning from Human-Generated Lists. - Kwang-Sung Jun; Xiaojin (Jerry) Zhu; Burr Settles; Timothy T. Rogers (ICML (3), 2013)

Human Memory Search as Initial-Visit Emitting Random Walk. - Kwang-Sung Jun; Xiaojin Zhu 001; Timothy T. Rogers; Zhuoran Yang; Ming Yuan 001 (NIPS, 2015)

Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. - Kwang-Sung Jun; Kevin G. Jamieson; Robert D. Nowak; Xiaojin Zhu 001 (AISTATS, 2016)

U-INVITE: Estimating Individual Semantic Networks from Fluency Data. - Jeffrey C. Zemla; Yoed N. Kenett; Kwang-Sung Jun; Joseph L. Austerweil (CogSci, 2016)

Graph-based active learning: A new look at expected error minimization. - Kwang-Sung Jun; Robert D. Nowak (GlobalSIP, 2016)

Anytime Exploration for Multi-armed Bandits using Confidence Information. - Kwang-Sung Jun; Robert D. Nowak (ICML, 2016)

Improved Strongly Adaptive Online Learning using Coin Betting. - Kwang-Sung Jun; Francesco Orabona; Stephen J. Wright 001; Rebecca Willett (AISTATS, 2017)

Identifying Multiple Authors in a Binary Program. - Xiaozhu Meng; Barton P. Miller; Kwang-Sung Jun (ESORICS (2), 2017)

Scalable Generalized Linear Bandits: Online Computation and Hashing. - Kwang-Sung Jun; Aniruddha Bhargava; Robert D. Nowak; Rebecca Willett (NIPS, 2017)

Online Learning for Changing Environments using Coin Betting. - Kwang-Sung Jun; Francesco Orabona; Stephen J. Wright 001; Rebecca Willett (CoRR, 2017)

Data Poisoning Attacks in Contextual Bandits. - Yuzhe Ma; Kwang-Sung Jun; Lihong Li 001; Xiaojin Zhu 001 (GameSec, 2018)

Adversarial Attacks on Stochastic Bandits. - Kwang-Sung Jun; Lihong Li 001; Yuzhe Ma; Xiaojin (Jerry) Zhu (NeurIPS, 2018)

Parameter-Free Online Convex Optimization with Sub-Exponential Noise. - Kwang-Sung Jun; Francesco Orabona (COLT, 2019)

Bilinear Bandits with Low-rank Structure. - Kwang-Sung Jun; Rebecca Willett; Stephen J. Wright 001; Robert D. Nowak (ICML, 2019)

Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. - Kwang-Sung Jun; Ashok Cutkosky; Francesco Orabona (NeurIPS, 2019)

Parameter-Free Locally Differentially Private Stochastic Subgradient Descent. - Kwang-Sung Jun; Francesco Orabona (CoRR, 2019)

Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality. - Kwang-Sung Jun; Chicheng Zhang (NeurIPS, 2020)

Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. - Kwang-Sung Jun; Lalit Jain; Houssam Nassif (CoRR, 2020)

Improved Regret Bounds of Bilinear Bandits using Action Space Analysis. - Kyoungseok Jang; Kwang-Sung Jun; Se-Young Yun; Wanmo Kang (ICML, 2021)

Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. - Kwang-Sung Jun; Lalit Jain; Houssam Nassif; Blake Mason (ICML, 2021)

An Experimental Design Approach for Regret Minimization in Logistic Bandits. - Blake Mason; Kwang-Sung Jun; Lalit Jain (AAAI, 2022)

Maillard Sampling: Boltzmann Exploration Done Optimally. - Jie Bian; Kwang-Sung Jun (AISTATS, 2022)

Jointly Efficient and Optimal Algorithms for Logistic Bandits. - Louis Faury; Marc Abeille; Kwang-Sung Jun; Clément Calauzènes (AISTATS, 2022)

Norm-Agnostic Linear Bandits. - Spencer B. Gales; Sunder Sethuraman; Kwang-Sung Jun (AISTATS, 2022)

PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits. - Kyoungseok Jang; Chicheng Zhang; Kwang-Sung Jun (NeurIPS, 2022)

Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs. - Yeoneung Kim; Insoon Yang; Kwang-Sung Jun (NeurIPS, 2022)

Revisiting Simple Regret Minimization in Multi-Armed Bandits. - Yao Zhao; Connor Stephens; Csaba Szepesvári; Kwang-Sung Jun (CoRR, 2022)

Tighter PAC-Bayes Bounds Through Coin-Betting. - Kyoungseok Jang; Kwang-Sung Jun; Ilja Kuzborskij; Francesco Orabona (COLT, 2023)

Revisiting Simple Regret: Fast Rates for Returning a Good Arm. - Yao Zhao; Connor Stephens; Csaba Szepesvári; Kwang-Sung Jun (ICML, 2023)

Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards. - Hao Qin; Kwang-Sung Jun; Chicheng Zhang (NeurIPS, 2023)

Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search. - Abu Reyan Ahmed; Mithun Ghosh; Kwang-Sung Jun; Stephen G. Kobourov (CoRR, 2023)

Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search. - Alvin Chiu; Mithun Ghosh; Abu Reyan Ahmed; Kwang-Sung Jun; Stephen G. Kobourov; Michael T. Goodrich (CoRR, 2023)

Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion. - Junghyun Lee; Se-Young Yun; Kwang-Sung Jun (AISTATS, 2024)

Better-than-KL PAC-Bayes Bounds. - Ilja Kuzborskij; Kwang-Sung Jun; Yulian Wu; Kyoungseok Jang; Francesco Orabona (COLT, 2024)

Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits. - Kyoungseok Jang; Chicheng Zhang; Kwang-Sung Jun (ICML, 2024)

Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization. - Kwang-Sung Jun; Jungtaek Kim 001 (ICML, 2024)

A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits. - Junghyun Lee; Se-Young Yun; Kwang-Sung Jun (NeurIPS, 2024)

Adaptive Experimentation When You Can't Experiment. - Yao Zhao; Kwang-Sung Jun; Tanner Fiez; Lalit Jain (NeurIPS, 2024)

HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning. - Tuan Ngo Nguyen; Kwang-Sung Jun (CoRR, 2024)

Fixing the Loose Brake: Exponential-Tailed Stopping Time in Best Arm Identification. - Kapilan Balagopalan; Tuan Ngo Nguyen; Yao Zhao; Kwang-Sung Jun (CoRR, 2024)

Tight Concentrations and Confidence Sequences From the Regret of Universal Portfolio. - Francesco Orabona; Kwang-Sung Jun (IEEE Trans. Inf. Theory, 2024)

Transfer Learning in Bandits With Latent Continuity. - Hyejin Park 002; Seiyun Shin; Kwang-Sung Jun; Jungseul Ok (IEEE Trans. Inf. Theory, 2024)

Minimum Empirical Divergence for Sub-Gaussian Linear Bandits. - Kapilan Balagopalan; Kwang-Sung Jun (AISTATS, 2025)

HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search. - Tuan Nguyen; Jay Barrett; Kwang-Sung Jun (AISTATS, 2025)

Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing. - J. Jon Ryu; Jeongyeol Kwon; Benjamin Koppe; Kwang-Sung Jun (COLT, 2025)

Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leiblier Maillard Sampling. - Hao Qin; Kwang-Sung Jun; Chicheng Zhang (CoRR, 2025)

Learning Explainable Dense Reward Shapes via Bayesian Optimization. - Ryan Koo; Ian Yang; Vipul Raheja; Mingyi Hong 001; Kwang-Sung Jun; Dongyeop Kang (CoRR, 2025)

Alignment as Distribution Learning: Your Preference Model is Explicitly a Language Model. - Jihun Yun; Juno Kim; Jongho Park; Junhyuck Kim; Jongha Jon Ryu; Jaewoong Cho; Kwang-Sung Jun (CoRR, 2025)

GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression. - Junghyun Lee; Kyoungseok Jang; Kwang-Sung Jun; Milan Vojnovic; Se-Young Yun (CoRR, 2025)

Second-Order Bounds for [0,1]-Valued Regression via Betting Loss. - Yinan Li; Kwang-Sung Jun (CoRR, 2025)