Kwang-Sung Jun
33 papers · 2012–2025 · 6 conferences · across top CS/AI conferences
Achievements
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🌍 Conference Polyglot (6) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🏃 Academic Marathon (13)
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Interdisciplinary Bridge
🌍
Conference Polyglot
(6)
🏃
Academic Marathon
(13)
🔬
Deep Specialist
(14)
🏆
Keyword Champion
(3)
🗃️
Keyword Collector
(102)
🚀
Conference Pioneer
💎
Century Club
(33)
🔥
Unstoppable
(11)
📈
Trend Setter
⚡
Prolific Year
(6)
Conferences
NIPS (10)
ICML (9)
AISTATS (8)
COLT (4)
AAAI (1)
NAACL (1)
Top co-authors
Keywords
regret bound
(14)
multi-armed bandit
(8)
online learning
(7)
logistic bandit
(4)
pure exploration
(4)
confidence bound
(3)
thompson sampling
(3)
linear bandit
(3)
experimental design
(3)
regret minimization
(3)
coin betting
(2)
bilinear bandit
(2)
confidence set
(2)
confidence sequence
(2)
pac-bayes bound
(2)
stochastic bandit
(2)
asymptotic optimality
(2)
stochastic optimization
(2)
maximum likelihood estimation
(2)
posterior distribution
(1)
Papers
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search
AISTATS 2025
Fixing the Loose Brake: Exponential-Tailed Stopping Time in Best Arm Identification
ICML 2025
Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing
COLT 2025
Minimum Empirical Divergence for Sub-Gaussian Linear Bandits
AISTATS 2025
Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization
ICML 2024
Adaptive Experimentation When You Can't Experiment
NIPS 2024
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
NIPS 2024
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
AISTATS 2024
Better-than-KL PAC-Bayes Bounds
COLT 2024
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits
ICML 2024
Revisiting Simple Regret: Fast Rates for Returning a Good Arm
ICML 2023
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards
NIPS 2023
Tighter PAC-Bayes Bounds Through Coin-Betting
COLT 2023
Jointly Efficient and Optimal Algorithms for Logistic Bandits
AISTATS 2022
An Experimental Design Approach for Regret Minimization in Logistic Bandits
AAAI 2022
Maillard Sampling: Boltzmann Exploration Done Optimally
AISTATS 2022
Norm-Agnostic Linear Bandits
AISTATS 2022
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs
NIPS 2022
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
NIPS 2022
Improved Regret Bounds of Bilinear Bandits using Action Space Analysis
ICML 2021
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits
ICML 2021
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality
NIPS 2020
Parameter-Free Online Convex Optimization with Sub-Exponential Noise
COLT 2019
Bilinear Bandits with Low-rank Structure
ICML 2019
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration
NIPS 2019
Adversarial Attacks on Stochastic Bandits
NIPS 2018
Improved Strongly Adaptive Online Learning using Coin Betting
AISTATS 2017
Scalable Generalized Linear Bandits: Online Computation and Hashing
NIPS 2017
Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls
AISTATS 2016
Anytime Exploration for Multi-armed Bandits using Confidence Information
ICML 2016
Human Memory Search as Initial-Visit Emitting Random Walk
NIPS 2015
Learning from Human-Generated Lists
ICML 2013
Learning from Bullying Traces in Social Media
NAACL 2012