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2020 – today
- 2024
[j2]Francesco Orabona
, Kwang-Sung Jun:
Tight Concentrations and Confidence Sequences From the Regret of Universal Portfolio. IEEE Trans. Inf. Theory 70(1): 436-455 (2024)- [j1]Hyejin Park, Seiyun Shin, Kwang-Sung Jun, Jungseul Ok:
Transfer Learning in Bandits With Latent Continuity. IEEE Trans. Inf. Theory 70(11): 7952-7970 (2024) - [c33]Junghyun Lee, Se-Young Yun, Kwang-Sung Jun:
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion. AISTATS 2024: 4474-4482 - [c32]Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang, Francesco Orabona:
Better-than-KL PAC-Bayes Bounds. COLT 2024: 3325-3352 - [c31]Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun:
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits. ICML 2024 - [c30]Kwang-Sung Jun, Jungtaek Kim:
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization. ICML 2024 - [i32]Kwang-Sung Jun, Jungtaek Kim:
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization. CoRR abs/2402.07341 (2024) - [i31]Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang, Francesco Orabona:
Better-than-KL PAC-Bayes Bounds. CoRR abs/2402.09201 (2024) - [i30]Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun:
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits. CoRR abs/2402.11156 (2024) - [i29]Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain:
Adaptive Experimentation When You Can't Experiment. CoRR abs/2406.10738 (2024) - [i28]Junghyun Lee, Se-Young Yun, Kwang-Sung Jun:
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits. CoRR abs/2407.13977 (2024) - [i27]Kapilan Balagopalan, Tuan Ngo Nguyen, Yao Zhao, Kwang-Sung Jun:
Fixing the Loose Brake: Exponential-Tailed Stopping Time in Best Arm Identification. CoRR abs/2411.01808 (2024) - 2023
- [c29]Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij, Francesco Orabona:
Tighter PAC-Bayes Bounds Through Coin-Betting. COLT 2023: 2240-2264 - [c28]Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun:
Revisiting Simple Regret: Fast Rates for Returning a Good Arm. ICML 2023: 42110-42158 - [c27]Hao Qin, Kwang-Sung Jun, Chicheng Zhang:
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards. NeurIPS 2023 - [i26]Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij, Francesco Orabona:
Tighter PAC-Bayes Bounds Through Coin-Betting. CoRR abs/2302.05829 (2023) - [i25]Hao Qin, Kwang-Sung Jun, Chicheng Zhang:
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards. CoRR abs/2304.14989 (2023) - [i24]Abu Reyan Ahmed, Mithun Ghosh, Kwang-Sung Jun, Stephen G. Kobourov:
Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search. CoRR abs/2305.00535 (2023) - [i23]Junghyun Lee, Se-Young Yun, Kwang-Sung Jun:
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion. CoRR abs/2310.18554 (2023) - [i22]Alvin Chiu, Mithun Ghosh, Abu Reyan Ahmed, Kwang-Sung Jun, Stephen G. Kobourov, Michael T. Goodrich:
Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search. CoRR abs/2311.10316 (2023) - 2022
- [c26]Blake Mason, Kwang-Sung Jun, Lalit Jain:
An Experimental Design Approach for Regret Minimization in Logistic Bandits. AAAI 2022: 7736-7743 - [c25]Jie Bian, Kwang-Sung Jun:
Maillard Sampling: Boltzmann Exploration Done Optimally. AISTATS 2022: 54-72 - [c24]Spencer B. Gales, Sunder Sethuraman, Kwang-Sung Jun:
Norm-Agnostic Linear Bandits. AISTATS 2022: 73-91 - [c23]Louis Faury, Marc Abeille, Kwang-Sung Jun, Clément Calauzènes:
Jointly Efficient and Optimal Algorithms for Logistic Bandits. AISTATS 2022: 546-580 - [c22]Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun:
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits. NeurIPS 2022 - [c21]Yeoneung Kim, Insoon Yang, Kwang-Sung Jun:
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs. NeurIPS 2022 - [i21]Louis Faury, Marc Abeille, Kwang-Sung Jun, Clément Calauzènes:
Jointly Efficient and Optimal Algorithms for Logistic Bandits. CoRR abs/2201.01985 (2022) - [i20]Blake Mason, Kwang-Sung Jun, Lalit Jain:
An Experimental Design Approach for Regret Minimization in Logistic Bandits. CoRR abs/2202.02407 (2022) - [i19]Spencer Gales, Sunder Sethuraman, Kwang-Sung Jun:
Norm-Agnostic Linear Bandits. CoRR abs/2205.01257 (2022) - [i18]Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun:
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits. CoRR abs/2210.15345 (2022) - [i17]Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun:
Revisiting Simple Regret Minimization in Multi-Armed Bandits. CoRR abs/2210.16913 (2022) - 2021
- [c20]Kyoungseok Jang, Kwang-Sung Jun, Se-Young Yun, Wanmo Kang:
Improved Regret Bounds of Bilinear Bandits using Action Space Analysis. ICML 2021: 4744-4754 - [c19]Kwang-Sung Jun, Lalit Jain, Houssam Nassif, Blake Mason:
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. ICML 2021: 5148-5157 - [c18]Hyejin Park, Seiyun Shin, Kwang-Sung Jun, Jungseul Ok:
Transfer Learning in Bandits with Latent Continuity. ISIT 2021: 1463-1468 - [i16]Hyejin Park, Seiyun Shin, Kwang-Sung Jun, Jungseul Ok:
Transfer Learning in Bandits with Latent Continuity. CoRR abs/2102.02472 (2021) - [i15]Francesco Orabona, Kwang-Sung Jun:
Tight Concentrations and Confidence Sequences from the Regret of Universal Portfolio. CoRR abs/2110.14099 (2021) - [i14]Yeoneung Kim, Insoon Yang, Kwang-Sung Jun:
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs. CoRR abs/2111.03289 (2021) - [i13]Jie Bian, Kwang-Sung Jun:
Maillard Sampling: Boltzmann Exploration Done Optimally. CoRR abs/2111.03290 (2021) - 2020
- [c17]Kwang-Sung Jun, Chicheng Zhang:
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality. NeurIPS 2020 - [i12]Kwang-Sung Jun, Chicheng Zhang:
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality. CoRR abs/2006.08754 (2020) - [i11]Kwang-Sung Jun, Lalit Jain, Houssam Nassif:
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. CoRR abs/2011.11222 (2020)
2010 – 2019
- 2019
- [c16]Kwang-Sung Jun, Francesco Orabona:
Parameter-Free Online Convex Optimization with Sub-Exponential Noise. COLT 2019: 1802-1823 - [c15]Kwang-Sung Jun, Rebecca Willett, Stephen J. Wright, Robert D. Nowak:
Bilinear Bandits with Low-rank Structure. ICML 2019: 3163-3172 - [c14]Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona:
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. NeurIPS 2019: 15332-15341 - [i10]Kwang-Sung Jun, Rebecca Willett, Stephen J. Wright, Robert D. Nowak:
Bilinear Bandits with Low-rank Structure. CoRR abs/1901.02470 (2019) - [i9]Kwang-Sung Jun, Francesco Orabona:
Parameter-free Online Convex Optimization with Sub-Exponential Noise. CoRR abs/1902.01500 (2019) - [i8]Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona:
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. CoRR abs/1905.10680 (2019) - [i7]Kwang-Sung Jun, Francesco Orabona:
Parameter-Free Locally Differentially Private Stochastic Subgradient Descent. CoRR abs/1911.09564 (2019) - 2018
- [c13]Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu:
Data Poisoning Attacks in Contextual Bandits. GameSec 2018: 186-204 - [c12]Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin (Jerry) Zhu:
Adversarial Attacks on Stochastic Bandits. NeurIPS 2018: 3644-3653 - [i6]Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu:
Data Poisoning Attacks in Contextual Bandits. CoRR abs/1808.05760 (2018) - [i5]Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu:
Adversarial Attacks on Stochastic Bandits. CoRR abs/1810.12188 (2018) - 2017
- [c11]Kwang-Sung Jun, Francesco Orabona, Stephen J. Wright, Rebecca Willett:
Improved Strongly Adaptive Online Learning using Coin Betting. AISTATS 2017: 943-951 - [c10]Xiaozhu Meng, Barton P. Miller, Kwang-Sung Jun:
Identifying Multiple Authors in a Binary Program. ESORICS (2) 2017: 286-304 - [c9]Kwang-Sung Jun, Aniruddha Bhargava, Robert D. Nowak, Rebecca Willett:
Scalable Generalized Linear Bandits: Online Computation and Hashing. NIPS 2017: 99-109 - [i4]Kwang-Sung Jun, Aniruddha Bhargava, Robert D. Nowak, Rebecca Willett:
Scalable Generalized Linear Bandits: Online Computation and Hashing. CoRR abs/1706.00136 (2017) - [i3]Kwang-Sung Jun, Francesco Orabona, Stephen J. Wright, Rebecca Willett:
Online Learning for Changing Environments using Coin Betting. CoRR abs/1711.02545 (2017) - 2016
- [c8]Kwang-Sung Jun, Kevin G. Jamieson, Robert D. Nowak, Xiaojin Zhu:
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. AISTATS 2016: 139-148 - [c7]Jeffrey C. Zemla, Yoed N. Kenett, Kwang-Sung Jun, Joseph L. Austerweil:
U-INVITE: Estimating Individual Semantic Networks from Fluency Data. CogSci 2016 - [c6]Kwang-Sung Jun, Robert D. Nowak:
Graph-based active learning: A new look at expected error minimization. GlobalSIP 2016: 1325-1329 - [c5]Kwang-Sung Jun, Robert D. Nowak:
Anytime Exploration for Multi-armed Bandits using Confidence Information. ICML 2016: 974-982 - [i2]Kwang-Sung Jun, Robert D. Nowak:
Graph-Based Active Learning: A New Look at Expected Error Minimization. CoRR abs/1609.00845 (2016) - [i1]Kwang-Sung Jun, Francesco Orabona, Rebecca Willett, Stephen J. Wright:
Improved Strongly Adaptive Online Learning using Coin Betting. CoRR abs/1610.04578 (2016) - 2015
- [c4]Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers, Zhuoran Yang, Ming Yuan:
Human Memory Search as Initial-Visit Emitting Random Walk. NIPS 2015: 1072-1080 - 2013
- [c3]Kwang-Sung Jun, Xiaojin (Jerry) Zhu, Burr Settles, Timothy T. Rogers:
Learning from Human-Generated Lists. ICML (3) 2013: 181-189 - 2012
- [c2]Jun-Ming Xu, Kwang-Sung Jun, Xiaojin Zhu, Amy Bellmore:
Learning from Bullying Traces in Social Media. HLT-NAACL 2012: 656-666 - 2010
- [c1]Xiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timothy T. Rogers, Joseph Harrison, Chuck Kalish:
Cognitive Models of Test-Item Effects in Human Category Learning. ICML 2010: 1247-1254
Coauthor Index
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last updated on 2024-12-11 21:40 CET by the dblp team
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