default search action
Kwang-Sung Jun
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
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 - [i31]Kwang-Sung Jun, Jungtaek Kim:
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization. CoRR abs/2402.07341 (2024) - [i30]Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang
Better-than-KL PAC-Bayes Bounds. CoRR abs/2402.09201 (2024) - [i29]Kyoungseok Jang
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits. CoRR abs/2402.11156 (2024) - [i28]Yao Zhao, Kwang-Sung Jun, Tanner Fiez, Lalit Jain:
Adaptive Experimentation When You Can't Experiment. CoRR abs/2406.10738 (2024) - [i27]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) - 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
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
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
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
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
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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-19 21:44 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: