| 2026 | COLT | Second-Order Bounds for [0, 1]-Valued Regression via Betting Loss. | Yinan Li, Sungjoon Yoon, Ethan Huang, Kwang-Sung Jun |
| 2025 | AISTATS | Minimum Empirical Divergence for Sub-Gaussian Linear Bandits. | Kapilan Balagopalan, Kwang-Sung Jun |
| 2025 | AISTATS | 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 |
| 2025 | COLT | Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing. | Jongha Jon Ryu, Jeongyeol Kwon, Benjamin Koppe, Kwang-Sung Jun |
| 2025 | ICML | Fixing the Loose Brake: Exponential-Tailed Stopping Time in Best Arm Identification. | Kapilan Balagopalan, Tuan Ngo Nguyen, Yao Zhao, Kwang-Sung Jun |
| 2024 | AISTATS | Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion. | Junghyun Lee, Se-Young Yun, Kwang-Sung Jun |
| 2024 | COLT | Better-than-KL PAC-Bayes Bounds. | Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang, Francesco Orabona |
| 2024 | ICML | Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits. | Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun |
| 2024 | ICML | Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization. | Kwang-Sung Jun, Jungtaek Kim |
| 2023 | COLT | Tighter PAC-Bayes Bounds Through Coin-Betting. | Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij, Francesco Orabona |
| 2023 | ICML | Revisiting Simple Regret: Fast Rates for Returning a Good Arm. | Yao Zhao, Connor Stephens, Csaba Szepesvri, Kwang-Sung Jun |
| 2022 | AAAI | An Experimental Design Approach for Regret Minimization in Logistic Bandits. | Blake Mason, Kwang-Sung Jun, Lalit Jain |
| 2022 | AISTATS | Maillard Sampling: Boltzmann Exploration Done Optimally. | Jie Bian, Kwang-Sung Jun |
| 2022 | AISTATS | Jointly Efficient and Optimal Algorithms for Logistic Bandits. | Louis Faury, Marc Abeille, Kwang-Sung Jun, Clment Calauznes |
| 2022 | AISTATS | Norm-Agnostic Linear Bandits. | Spencer B. Gales, Sunder Sethuraman, Kwang-Sung Jun |
| 2021 | ICML | Improved Regret Bounds of Bilinear Bandits using Action Space Analysis. | Kyoungseok Jang, Kwang-Sung Jun, Se-Young Yun, Wanmo Kang |
| 2021 | ICML | Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. | Kwang-Sung Jun, Lalit Jain, Houssam Nassif, Blake Mason |
| 2021 | ISIT | Transfer Learning in Bandits with Latent Continuity. | Hyejin Park, Seiyun Shin, Kwang-Sung Jun, Jungseul Ok |
| 2019 | COLT | Parameter-Free Online Convex Optimization with Sub-Exponential Noise. | Kwang-Sung Jun, Francesco Orabona |
| 2019 | ICML | Bilinear Bandits with Low-rank Structure. | Kwang-Sung Jun, Rebecca Willett, Stephen J. Wright, Robert D. Nowak |
| 2017 | AISTATS | Improved Strongly Adaptive Online Learning using Coin Betting. | Kwang-Sung Jun, Francesco Orabona, Stephen J. Wright, Rebecca Willett |
| 2017 | ESORICS | Identifying Multiple Authors in a Binary Program. | Xiaozhu Meng, Barton P. Miller, Kwang-Sung Jun |
| 2016 | AISTATS | Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. | Kwang-Sung Jun, Kevin Jamieson, Robert D. Nowak, Xiaojin Zhu |
| 2016 | CogSci | U-INVITE: Estimating Individual Semantic Networks from Fluency Data. | Jeffrey C. Zemla, Yoed N. Kenett, Kwang-Sung Jun, Joseph L. Austerweil |
| 2016 | ICML | Anytime Exploration for Multi-armed Bandits using Confidence Information. | Kwang-Sung Jun, Robert D. Nowak |
| 2013 | ICML | Learning from Human-Generated Lists. | Kwang-Sung Jun, Xiaojin (Jerry) Zhu, Burr Settles, Timothy T. Rogers |
| 2012 | NAACL | Learning from Bullying Traces in Social Media. | Jun-Ming Xu, Kwang-Sung Jun, Xiaojin Zhu, Amy Bellmore |
| 2010 | ICML | Cognitive Models of Test-Item Effects in Human Category Learning. | Xiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timothy T. Rogers, Joseph Harrison, Chuck Kalish |