Statistics

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

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

Nearly Optimal Latent State Decoding in Block MDPs

First theoretical analysis of model estimation and reward-free RL of block MDP, without resorting to function approximation frameworks. Lower bounds and algorithms with near-optimal upper bound are provided.

Yassir Jedra, Junghyun Lee, Alexandre Proutière, Se-Young Yun

Nearly Optimal Latent State Decoding in Block MDPs

Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold

Proposes a new MMD-based definition of fairness for PCA, then formulate fair PCA as an optimization over the Stiefel manifold. Various theoretical and empirical discussions show the superiority of our approach compared to the existing approach (Olfat & Aswani, AAAI'19).

Junghyun Lee, Gwangsu Kim, Matt Olfat, Mark Hasegawa-Johnson, Chang D. Yoo