1 citations · 4 across the 8 of their papers we have counts for
8 papers
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.…
Querying Easily Flip-flopped Samples for Deep Active Learning
Seong Jin Cho, Gwangsu Kim, Junghyun Lee +2
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strat…
Gradient Descent with Polyak's Momentum Finds Flatter Minima via Large Catapults
Prin Phunyaphibarn, Junghyun Lee, Bohan Wang +2
Although gradient descent with Polyak's momentum is widely used in modern machine and deep learning, a concrete understanding of its effects on the training trajectory remains elus…
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
Junghyun Lee, Hanseul Cho, Se-Young Yun +1
Fair Principal Component Analysis (PCA) is a problem setting where we aim to perform PCA while making the resulting representation fair in that the projected distributions, conditi…
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
Logistic bandit is a ubiquitous framework of modeling users' choices, e.g., click vs. no click for advertisement recommender system. We observe that the prior works overlook or neg…
Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks
Junghyun Lee, Laura Schmid, Se-Young Yun
Multi-armed bandits are extensively used to model sequential decision-making, making them ubiquitous in many real-life applications such as online recommender systems and wireless…
Nearly Optimal Latent State Decoding in Block MDPs
Yassir Jedra, Junghyun Lee, Alexandre Proutière +1
We investigate the problems of model estimation and reward-free learning in episodic Block MDPs. In these MDPs, the decision maker has access to rich observations or contexts gener…
Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold
Junghyun Lee, Gwangsu Kim, Matt Olfat +2
This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between dimensionality-reduced conditional distributions of different pr…