Se-Young Yun

GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression
We present GL-LowPopArt, a novel Catoni-style estimator for generalized low-rank trace regression. Building on LowPopArt (Jang et al., …
Junghyun Lee, Kyoungseok Jang, Kwang-Sung Jun, Milan Vojnović, Se-Young Yun
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GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression
Near-Optimal Clustering in Mixture of Markov Chains
We study the problem of clustering T trajectories of length H, each generated by one of K unknown ergodic Markov chains over a finite …
Junghyun Lee, Yassir Jedra, Alexandre Proutière, Se-Young Yun
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Near-Optimal Clustering in Mixture of Markov Chains
Regularized Online RLHF with Generalized Bilinear Preferences
We consider the problem of contextual online RLHF with general preferences, where the goal is to identify the Nash Equilibrium. We …
Junghyun Lee, Minju Hong, Kwang-Sung Jun, Chulhee Yun, Se-Young Yun
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Regularized Online RLHF with Generalized Bilinear Preferences
A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
We consider the problem of heteroskedastic generalized linear bandits (GLBs) with adversarial corruptions, which subsumes various …
Sanghwa Kim, Junghyun Lee, Se-Young Yun
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A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners
Self-Taught Reasoners (STaR), synonymously known as Rejection sampling Fine-Tuning (RFT), is an integral part of the training pipeline …
Woosung Koh, Wonbeen Oh, Jaein Jang, MinHyung Lee, Hyeongjin Kim, Ah Yeon Kim, Joonkee Kim, Junghyun Lee, Taehyeon Kim, Se-Young Yun
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AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners
Probability-Flow ODE in Infinite-Dimensional Function Spaces
Recent advances in infinite-dimensional diffusion models have demonstrated their effectiveness and scalability in function generation …
Kunwoo Na, Junghyun Lee, Se-Young Yun, Sungbin Lim
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Probability-Flow ODE in Infinite-Dimensional Function Spaces
FlickerFusion: Intra-trajectory Domain Generalizing Multi-Agent RL
Multi-agent reinforcement learning has demonstrated significant potential in addressing complex cooperative tasks across various …
Woosung Koh, Wonbeen Oh, Siyeol Kim, Suhin Shin, Hyeongjin Kim, Jaein Jang, Junghyun Lee, Se-Young Yun
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FlickerFusion: Intra-trajectory Domain Generalizing Multi-Agent RL
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
We present a unified likelihood ratio-based confidence sequence (CS) for any (self-concordant) generalized linear model (GLM) that is …
Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
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A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
Logistic bandit is a ubiquitous framework of modeling users’ choices, e.g., click vs. no click for advertisement recommender …
Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
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Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
Proposes a framework for performing fair PCA in memory limited, streaming setting. Sample complexity results and empirical discussions show the superiority of our approach compared to the existing approaches.
Junghyun Lee, Hanseul Cho, Se-Young Yun, Chulhee Yun
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Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint