6 papers
A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
Sanghwa Kim, Junghyun Lee, Se-Young Yun
We consider the problem of heteroskedastic generalized linear bandits (GLBs) with adversarial corruptions, which subsumes heteroskedastic linear bandits and logistic/Poisson bandit…
Instance-Optimal Estimation with Multiple LLM Judges on a Budget
Junghyun Lee, Sanghwa Kim, Yassir Jedra +2
Evaluating large language models increasingly relies on LLM-as-a-judge protocols, but such evaluations remain costly: different judges have different prices and reliabilities, and…
GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression
Junghyun Lee, Kyoungseok Jang, Kwang-Sung Jun +2
We present `GL-LowPopArt`, a novel Catoni-style estimator for generalized low-rank trace regression. Building on `LowPopArt` (Jang et al., 2024), it employs a two-stage approach: n…
Near-Optimal Clustering in Mixture of Markov Chains
Junghyun Lee, Yassir Jedra, Alexandre Proutière +1
We study the problem of clustering trajectories of length , each generated by one of K unknown ergodic Markov chains over a finite state space of size . We derive an inst…
Probability-Flow ODE in Infinite-Dimensional Function Spaces
Kunwoo Na, Junghyun Lee, Se-Young Yun +1
Recent advances in infinite-dimensional diffusion models have demonstrated their effectiveness and scalability in function generation tasks where the underlying structure is inhere…
A Unified Confidence Sequence for Generalized Linear Models, with Applications to Bandits
Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
We present a unified likelihood ratio-based confidence sequence (CS) for any (self-concordant) generalized linear model (GLM) that is guaranteed to be convex and numerically tight.…