PublicationsPreprintsEfficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun [arxiv] Better-than-KL PAC-Bayes Bounds Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang, Francesco Orabona [arxiv] Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization Kwang-Sung Jun, Jungtaek Kim [arxiv] 2024Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion Junghyun Lee, Se-Young Yun, Kwang-Sung Jun In International Conference on Artificial Intelligence and Statistics (AISTATS), 2024 [arxiv] Tight Concentrations and Confidence Sequences from the Regret of Universal Portfolio Francesco Orabona, Kwang-Sung Jun IEEE Transactions on Information Theory, 2024 [official] [arxiv] 2023Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards Hao Qin, Kwang-Sung Jun, Chicheng Zhang In Neural Information Processing Systems (NeurIPS), 2023 [official] [arxiv] [open review] Revisiting Simple Regret: Fast Rates for Returning a Good Arm Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun In International Conference on Machine Learning (ICML), 2023 [official] [arxiv] Tighter PAC-Bayes Bounds Through Coin-Betting Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij, Francesco Orabona (alphabetical order) In Conference on Learning Theory (COLT), 2023 [official] [arxiv] 2022PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun In Neural Information Processing Systems (NeurIPS), 2022. [official] [openreview] [arxiv] Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs Yeoneung Kim, Insoon Yang, Kwang-Sung Jun In Neural Information Processing Systems (NeurIPS), 2022 [official] [openreview] [arxiv] Jointly Efficient and Optimal Algorithms for Logistic Bandits Louis Faury, Marc Abeille, Kwang-Sung Jun, Clément Calauzènes In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 [official] [arxiv] Norm-Agnostic Linear Bandits Spencer Brady Gales, Sunder Sethuraman, Kwang-Sung Jun In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 [official] [arxiv] Maillard Sampling: Boltzmann Exploration Done Optimally Jie Bian, Kwang-Sung Jun In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 [official] [arxiv] An Experimental Design Approach for Regret Minimization in Logistic Bandits Blake Mason, Kwang-Sung Jun, Lalit Jain In AAAI Conference on Artificial Intelligence (AAAI), 2022 [official] [arxiv] 2021Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits Kwang-Sung Jun, Lalit Jain, Blake Mason, Houssam Nassif In International Conference on Machine Learning (ICML), 2021 [official] Improved Regret Bounds of Bilinear Bandits using Action Space Dimension Analysis Kyoungseok Jang, Kwang-Sung Jun, Se Young Yun, Wanmo Kang In International Conference on Machine Learning (ICML), 2021 [official] Transfer Learning in Bandits with Latent Continuity Hyejin Park, Seiyun Shin, Kwang-Sung Jun, Jungseul Ok In IEEE International Symposium on Information Theory (ISIT), 2021 [official] 2020Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality Kwang-Sung Jun, Chicheng Zhang In Neural Information Processing Systems (NeurIPS), 2020 [official] [arxiv] [slides] [video]
2019Parameter-Free Locally Differentially Private Stochastic Subgradient Descent Kwang-Sung Jun, Francesco Orabona In NeurIPS Workshop on Privacy in Machine Learning (PriML), 2019 [arxiv] [poster] Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona In Neural Information Processing Systems (NeurIPS), 2019 [official] [arxiv] [slides] [poster] Parameter-Free Online Convex Optimization with Sub-Exponential Noise Kwang-Sung Jun, Francesco Orabona In Conference on Learning Theory (COLT), 2019 [official] [arxiv] Bilinear Bandits with Low-rank Structure Kwang-Sung Jun, Rebecca Willett, Stephen Wright, Robert Nowak In International Conference on Machine Learning (ICML), 2019 [official] [arxiv] [code] 2018Adversarial Attacks on Stochastic Bandits Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu In Neural Information Processing Systems (NeurIPS), 2018 [official] [arxiv] Data Poisoning Attacks in Contextual Bandits Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu In Conference on Decision and Game Theory for Security (GameSec), 2018 [official] [arxiv] Bayesian Active Learning on Graphs Kwang-Sung Jun, Robert Nowak In Cooperative and Graph Signal Processing, Petar Djuric and Cedric Richard, Eds., Elsevier, 2018 [official] 2017Online Learning for Changing Environments using Coin Betting Kwang-Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett In Electronic Journal of Statistics (EJS), 2017 [official]
Scalable Generalized Linear Bandits: Online Computation and Hashing Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak, and Rebecca Willett In Neural Information Processing Systems (NeurIPS), 2017 [official] [arxiv] [code] Identifying Multiple Authors in a Binary Program Xiaozhu Meng, Barton P. Miller, and Kwang-Sung Jun. In European Symposium on Research in Computer Security (ESORICS), 2017 [official] 2016Graph-Based Active Learning: A New Look at Expected Error Minimization. Kwang-Sung Jun and Robert Nowak In IEEE GlobalSIP Symposium on Non-Commutative Theory and Applications, 2016 [ieee] [arxiv] U-INVITE: Estimating Individual Semantic Networks from Fluency Data Jeffrey Zemla, Yoed Kenett, Kwang-Sung Jun, and Joseph Austerweil. In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci), 2016 [pdf] Anytime Exploration for Multi-armed Bandits using Confidence Information Kwang-Sung Jun and Robert Nowak In International Conference on Machine Learning (ICML), 2016 [official] [post-publication note] Top arm identification in multi-armed bandits with batch arm pulls. Kwang-Sung Jun, Kevin Jamieson, Robert Nowak, and Xiaojin Zhu In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016 [official] 2015 and beforeHuman memory search as initial-visit emitting random walk. Kwang-Sung Jun, Xiaojin Zhu, Timothy Rogers, Zhuoran Yang, and Ming Yuan. In Neural Information Processing Systems (NeurIPS), 2015 [official] Smarter Crisis Crowdsourcing. Kayla Jacobs, Kwang-Sung Jun, Nathan Lieby, and Elena Eneva. In ACM SIGKDD Workshop on Data Science for Social Good, 2014 [pdf] Learning from Human-Generated Lists. Kwang-Sung Jun, Xiaojin Zhu, Burr Settles, and Timothy Rogers In International Conference on Machine Learning (ICML), 2013 [official] [code&data] [video] An Image-To-Speech iPad App. Michael Maynord, Jitrapon Tiachunpun, Xiaojin Zhu, Charles R. Dyer, Kwang-Sung Jun, and Jake Rosin. Department of Computer Sciences Technical Report TR1774, University of Wisconsin-Madison, 2012. Learning from bullying traces in social media. Jun-Ming Xu, Kwang-Sung Jun, Xiaojin Zhu, and Amy Bellmore In the Conference of North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT), 2012 [official] With a little help from the computer: Hybrid human-machine systems on bandit problems Bryan Gibson, Kwang-Sung Jun, and Xiaojin Zhu. In NeurIPS Workshop on Computational Social Science and the Wisdom of Crowds, 2010. [pdf] Cognitive models of test-item effects in human category learning Xiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timothy T. Rogers, Joseph Harrison, and Chuck Kalish. In International Conference on Machine Learning (ICML), 2010. [acm digital library] [pdf] An efficient collaborative filtering method based on k-nearest neighbor learning for large-scale data Kwang-Sung Jun and Kyu-Baek Hwang. In Proceedings of Korea Computer Congress, 2008. |