Kwang-Sung Jun

activity

20242026

collaborators

4 papers

hep-ph2026

Displaced tracks as a signal of dark matter co-annihilation: a case study using inert doublet model at $e^{+} e^{-}$ collider

Hrishikesh Deka, Subhaditya Bhattacharya, Amir Subba +1

Dark sector containing heavy (charged) particles with small mass splitting with dark matter (DM) can co-annihilate and produce correct relic density, evading direct detection bound…

cs.LG2025

Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling

Hao Qin, Kwang-Sung Jun, Chicheng Zhang

We study the problem of $K$ -armed bandits with reward distributions belonging to a one-parameter exponential distribution family. In the literature, several criteria have been prop…

stat.ML2024

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…

cs.LG2024

Better-than-KL PAC-Bayes Bounds

Ilja Kuzborskij, Kwang-Sung Jun, Yulian Wu +2

Let $f (θ, X_{1}),$ $\dots,$ $f (θ, X_{n})$ be a sequence of random elements, where $f$ is a fixed scalar function, $X_{1}, \dots, X_{n}$ are independent random variables (data), and $θ$ i…

Displaced tracks as a signal of dark matter co-annihilation: a case study using inert doublet model at e+e− collider

Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling

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

Better-than-KL PAC-Bayes Bounds

Displaced tracks as a signal of dark matter co-annihilation: a case study using inert doublet model at $e^{+} e^{-}$ collider