2 citations · 2 across the 5 of their papers we have counts for
6 papers
Beyond Second-Order Information: Paid Logistic Regression with Noisy Features
Sanghwa Kim, Kyoungseok Jang, Se-Young Yun
We consider online binary prediction with paid stochastic features, where the learner can spend resources to obtain better observations of the underlying features. This setting is…
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…
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits
Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun
We study low-rank matrix trace regression and the related problem of low-rank matrix bandits. Assuming access to the distribution of the covariates, we propose a novel low-rank mat…
Better-than-KL PAC-Bayes Bounds
Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu +2
Let be a sequence of random elements, where is a fixed scalar function, are independent random variables (data), and i…
Tighter PAC-Bayes Bounds Through Coin-Betting
Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij +1
We consider the problem of estimating the mean of a sequence of random elements where is a fixed scalar function, ar…
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun
In sparse linear bandits, a learning agent sequentially selects an action and receive reward feedback, and the reward function depends linearly on a few coordinates of the covariat…