| Learning from bullying traces in social media JM Xu, KS Jun, X Zhu, A Bellmore Proceedings of the 2012 conference of the North American chapter of the …, 2012 | 562 | 2012 |
| Adversarial attacks on stochastic bandits KS Jun, L Li, Y Ma, J Zhu Advances in Neural Information Processing Systems (NeurIPS), 3640-3649, 2018 | 171 | 2018 |
| Scalable Generalized Linear Bandits: Online Computation and Hashing KS Jun, A Bhargava, R Nowak, R Willett Advances in Neural Information Processing Systems (NIPS), 98-108, 2017 | 163 | 2017 |
| Top arm identification in multi-armed bandits with batch arm pulls KS Jun, K Jamieson, R Nowak, X Zhu Proceedings of the International Conference on Artificial Intelligence and …, 2016 | 109 | 2016 |
| Tight concentrations and confidence sequences from the regret of universal portfolio F Orabona, KS Jun IEEE Transactions on Information Theory 70 (1), 436-455, 2023 | 96 | 2023 |
| Data poisoning attacks in contextual bandits Y Ma, KS Jun, L Li, X Zhu International Conference on Decision and Game Theory for Security (GameSec …, 2018 | 95 | 2018 |
| Improved Strongly Adaptive Online Learning using Coin Betting KS Jun, F Orabona, R Willett, S Wright Proceedings of the International Conference on Artificial Intelligence and …, 2017 | 93 | 2017 |
| Bilinear Bandits with Low-rank Structure KS Jun, R Willett, S Wright, R Nowak Proceedings of the International Conference on Machine Learning (ICML), 3163 …, 2019 | 87 | 2019 |
| Parameter-free Online Convex Optimization with Sub-Exponential Noise KS Jun, F Orabona Conference on Learning Theory (COLT), 1802-1823, 2019 | 67 | 2019 |
| Identifying multiple authors in a binary program X Meng, BP Miller, KS Jun European Symposium on Research in Computer Security, 286-304, 2017 | 63 | 2017 |
| Improved confidence bounds for the linear logistic model and applications to bandits KS Jun, L Jain, B Mason, H Nassif International Conference on Machine Learning, 5148-5157, 2021 | 48* | 2021 |
| Jointly efficient and optimal algorithms for logistic bandits L Faury, M Abeille, KS Jun, C Calauzènes International Conference on Artificial Intelligence and Statistics, 546-580, 2022 | 44 | 2022 |
| Cognitive models of test-item effects in human category learning X Zhu, BR Gibson, KS Jun, TT Rogers, J Harrison, C Kalish Proceedings of the International Conference on Machine Learning (ICML), 1247 …, 2010 | 42 | 2010 |
| Tighter PAC-Bayes bounds through coin-betting K Jang, KS Jun, I Kuzborskij, F Orabona The Thirty Sixth Annual Conference on Learning Theory, 2240-2264, 2023 | 38 | 2023 |
| Improved regret analysis for variance-adaptive linear bandits and horizon-free linear mixture mdps Y Kim, I Yang, KS Jun Advances in Neural Information Processing Systems 35, 1060-1072, 2022 | 32 | 2022 |
| Revisiting Simple Regret: Fast Rates for Returning a Good Arm Y Zhao, C Stephens, C Szepesvari, KS Jun Proceedings of the International Conference on Machine Learning (ICML), 2023 | 31 | 2023 |
| Anytime Exploration for Multi-armed Bandits using Confidence Information KS Jun, R Nowak Proceedings of the International Conference on Machine Learning (ICML), 974-982, 2016 | 27 | 2016 |
| Graph-Based Active Learning: A New Look at Expected Error Minimization KS Jun, R Nowak IEEE GlobalSIP, 2016 | 25 | 2016 |
| A unified confidence sequence for generalized linear models, with applications to bandits J Lee, SY Yun, KS Jun arXiv preprint arXiv:2407.13977, 2024 | 24 | 2024 |
| Improved regret bounds of (multinomial) logistic bandits via regret-to-confidence-set conversion J Lee, SY Yun, KS Jun International Conference on Artificial Intelligence and Statistics, 4474-4482, 2024 | 24 | 2024 |
Xiaojin ZhuProfessor in Computer Science at University of Wisconsin-MadisonVerified email at cs.wisc.edu
