Kwang-Sung Jun

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 59

Top Topics

Generalized Linear

Top Venues

Publications

Francesco Orabona, Kwang-Sung Jun
Tight Concentrations and Confidence Sequences From the Regret of Universal Portfolio. IEEE Trans. Inf. Theory 70 (1) (2024)
Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain
Adaptive Experimentation When You Can't Experiment. CoRR (2024)
Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang, Francesco Orabona
Better-than-KL PAC-Bayes Bounds. COLT (2024)
Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion. AISTATS (2024)
Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang, Francesco Orabona
Better-than-KL PAC-Bayes Bounds. CoRR (2024)
Kwang-Sung Jun, Jungtaek Kim
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization. CoRR (2024)
Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits. CoRR (2024)
Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits. CoRR (2024)
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 (2023)
Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion. CoRR (2023)
Hao Qin, Kwang-Sung Jun, Chicheng Zhang
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards. CoRR (2023)
Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun
Revisiting Simple Regret: Fast Rates for Returning a Good Arm. ICML (2023)
Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij, Francesco Orabona
Tighter PAC-Bayes Bounds Through Coin-Betting. COLT (2023)
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 (2023)
Hao Qin, Kwang-Sung Jun, Chicheng Zhang
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards. NeurIPS (2023)
Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij, Francesco Orabona
Tighter PAC-Bayes Bounds Through Coin-Betting. CoRR (2023)
Louis Faury, Marc Abeille, Kwang-Sung Jun, Clément Calauzènes
Jointly Efficient and Optimal Algorithms for Logistic Bandits. CoRR (2022)
Yeoneung Kim, Insoon Yang, Kwang-Sung Jun
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs. NeurIPS (2022)
Spencer B. Gales, Sunder Sethuraman, Kwang-Sung Jun
Norm-Agnostic Linear Bandits. AISTATS (2022)
Spencer Gales, Sunder Sethuraman, Kwang-Sung Jun
Norm-Agnostic Linear Bandits. CoRR (2022)
Jie Bian, Kwang-Sung Jun
Maillard Sampling: Boltzmann Exploration Done Optimally. AISTATS (2022)
Blake Mason, Kwang-Sung Jun, Lalit Jain
An Experimental Design Approach for Regret Minimization in Logistic Bandits. AAAI (2022)
Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits. CoRR (2022)
Yao Zhao, Connor Stephens, Csaba Szepesvári, Kwang-Sung Jun
Revisiting Simple Regret Minimization in Multi-Armed Bandits. CoRR (2022)
Louis Faury, Marc Abeille, Kwang-Sung Jun, Clément Calauzènes
Jointly Efficient and Optimal Algorithms for Logistic Bandits. AISTATS (2022)
Blake Mason, Kwang-Sung Jun, Lalit Jain
An Experimental Design Approach for Regret Minimization in Logistic Bandits. CoRR (2022)
Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits. NeurIPS (2022)
Hyejin Park, Seiyun Shin, Kwang-Sung Jun, Jungseul Ok
Transfer Learning in Bandits with Latent Continuity. CoRR (2021)
Jie Bian, Kwang-Sung Jun
Maillard Sampling: Boltzmann Exploration Done Optimally. CoRR (2021)
Kwang-Sung Jun, Lalit Jain, Houssam Nassif, Blake Mason
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. ICML (2021)
Kyoungseok Jang, Kwang-Sung Jun, Se-Young Yun, Wanmo Kang
Improved Regret Bounds of Bilinear Bandits using Action Space Analysis. ICML (2021)
Francesco Orabona, Kwang-Sung Jun
Tight Concentrations and Confidence Sequences from the Regret of Universal Portfolio. CoRR (2021)
Hyejin Park, Seiyun Shin, Kwang-Sung Jun, Jungseul Ok
Transfer Learning in Bandits with Latent Continuity. ISIT (2021)
Yeoneung Kim, Insoon Yang, Kwang-Sung Jun
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs. CoRR (2021)
Kwang-Sung Jun, Chicheng Zhang
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality. NeurIPS (2020)
Kwang-Sung Jun, Lalit Jain, Houssam Nassif
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits. CoRR (2020)
Kwang-Sung Jun, Chicheng Zhang
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality. CoRR (2020)
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. CoRR (2019)
Kwang-Sung Jun, Francesco Orabona
Parameter-Free Online Convex Optimization with Sub-Exponential Noise. COLT (2019)
Kwang-Sung Jun, Rebecca Willett, Stephen J. Wright, Robert D. Nowak
Bilinear Bandits with Low-rank Structure. CoRR (2019)
Kwang-Sung Jun, Rebecca Willett, Stephen J. Wright, Robert D. Nowak
Bilinear Bandits with Low-rank Structure. ICML (2019)
Kwang-Sung Jun, Francesco Orabona
Parameter-Free Locally Differentially Private Stochastic Subgradient Descent. CoRR (2019)
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. NeurIPS (2019)
Kwang-Sung Jun, Francesco Orabona
Parameter-free Online Convex Optimization with Sub-Exponential Noise. CoRR (2019)
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu
Adversarial Attacks on Stochastic Bandits. CoRR (2018)
Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu
Data Poisoning Attacks in Contextual Bandits. GameSec (2018)
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin (Jerry) Zhu
Adversarial Attacks on Stochastic Bandits. NeurIPS (2018)
Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu
Data Poisoning Attacks in Contextual Bandits. CoRR (2018)
Kwang-Sung Jun, Francesco Orabona, Stephen J. Wright, Rebecca Willett
Online Learning for Changing Environments using Coin Betting. CoRR (2017)
Xiaozhu Meng, Barton P. Miller, Kwang-Sung Jun
Identifying Multiple Authors in a Binary Program. ESORICS (2) (2017)
Kwang-Sung Jun, Aniruddha Bhargava, Robert D. Nowak, Rebecca Willett
Scalable Generalized Linear Bandits: Online Computation and Hashing. NIPS (2017)
Kwang-Sung Jun, Francesco Orabona, Stephen J. Wright, Rebecca Willett
Improved Strongly Adaptive Online Learning using Coin Betting. AISTATS (2017)
Kwang-Sung Jun, Aniruddha Bhargava, Robert D. Nowak, Rebecca Willett
Scalable Generalized Linear Bandits: Online Computation and Hashing. CoRR (2017)
Kwang-Sung Jun, Robert D. Nowak
Graph-based active learning: A new look at expected error minimization. GlobalSIP (2016)
Kwang-Sung Jun, Robert D. Nowak
Anytime Exploration for Multi-armed Bandits using Confidence Information. ICML (2016)
Kwang-Sung Jun, Robert D. Nowak
Graph-Based Active Learning: A New Look at Expected Error Minimization. CoRR (2016)
Jeffrey C. Zemla, Yoed N. Kenett, Kwang-Sung Jun, Joseph L. Austerweil
U-INVITE: Estimating Individual Semantic Networks from Fluency Data. CogSci (2016)
Kwang-Sung Jun, Kevin G. Jamieson, Robert D. Nowak, Xiaojin Zhu
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. AISTATS (2016)
Kwang-Sung Jun, Francesco Orabona, Rebecca Willett, Stephen J. Wright
Improved Strongly Adaptive Online Learning using Coin Betting. CoRR (2016)
Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers, Zhuoran Yang, Ming Yuan
Human Memory Search as Initial-Visit Emitting Random Walk. NIPS (2015)
Kwang-Sung Jun, Xiaojin (Jerry) Zhu, Burr Settles, Timothy T. Rogers
Learning from Human-Generated Lists. ICML (3) (2013)
Jun-Ming Xu, Kwang-Sung Jun, Xiaojin Zhu, Amy Bellmore
Learning from Bullying Traces in Social Media. HLT-NAACL (2012)
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)