Kyoungseok Jang

According to our database, Kyoungseok Jang authored at least 8 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal Estimator for Generalized Low-Rank Trace Regression.

Junghyun Lee, Kyoungseok Jang, Kwang-Sung Jun, Milan Vojnovic, Se-Young Yun

CoRR, June, 2025

2024

Sparsity-Agnostic Linear Bandits with Adaptive Adversaries.

Tianyuan Jin, Kyoungseok Jang, Nicolò Cesa-Bianchi

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Fixed Confidence Best Arm Identification in the Bayesian Setting.

Kyoungseok Jang, Junpei Komiyama, Kazutoshi Yamazaki

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits.

Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Better-than-KL PAC-Bayes Bounds.

Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu, Kyoungseok Jang, Francesco Orabona

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Tighter PAC-Bayes Bounds Through Coin-Betting.

Kyoungseok Jang, Kwang-Sung Jun, Ilja Kuzborskij, Francesco Orabona

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits.

Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Improved Regret Bounds of Bilinear Bandits using Action Space Analysis.

Kyoungseok Jang, Kwang-Sung Jun, Se-Young Yun, Wanmo Kang

Proceedings of the 38th International Conference on Machine Learning, 2021