publications

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. [arxiv]

Parameter-Free Online Convex Optimization with Sub-Exponential Noise.
Kwang-Sung Jun, Francesco Orabona.
In Conference on Learning Theory (COLT), 2019. [official] [arxiv]

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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]

Adversarial Attacks on Stochastic Bandits.
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu.
In Neural Information Processing Systems (NeurIPS), 2018. [arxiv] [official]

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. [arxiv] [official]

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]

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Online Learning for Changing Environments using Coin Betting.
Kwang-Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett.
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]

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.

Improved Strongly Adaptive Online Learning using Coin Betting.
Kwang-Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett.
In International Conference on Artificial Intelligence and Statistics (AISTATS), 2017. Oral presentation. [official] [arxiv]

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Graph-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. [pdf]

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]

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Human 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. [pdf]

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. [pdf] [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. [pdf]

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. [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.