21 citations · 26 across the 6 of their papers we have counts for
6 papers
A diffusion-based generative model for financial time series via geometric Brownian motion
Gihun Kim, Sun-Yong Choi, Yeoneung Kim
We propose a novel diffusion-based generative framework for financial time series that incorporates geometric Brownian motion (GBM), the foundation of the Black--Scholes theory, in…
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with Regret
Yeoneung Kim, Gihun Kim, Jiwhan Park +1
We propose a novel Thompson sampling algorithm that learns linear quadratic regulators (LQR) with a Bayesian regret bound of . Our method leverages Langevin dynamics w…
On representation formulas for optimal control: A Lagrangian perspective
Yeoneung Kim, Insoon Yang
In this paper, we study representation formulas for finite-horizon optimal control problems with or without state constraints, unifying two different viewpoints: the Lagrangian and…
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs
Yeoneung Kim, Insoon Yang, Kwang-Sung Jun
In online learning problems, exploiting low variance plays an important role in obtaining tight performance guarantees yet is challenging because variances are often not known a pr…
Training Wasserstein GANs without gradient penalties
Dohyun Kwon, Yeoneung Kim, Guido Montúfar +1
We propose a stable method to train Wasserstein generative adversarial networks. In order to enhance stability, we consider two objective functions using the -transform based on…
Infusing model predictive control into meta-reinforcement learning for mobile robots in dynamic environments
Jaeuk Shin, Astghik Hakobyan, Mingyu Park +3
The successful operation of mobile robots requires them to adapt rapidly to environmental changes. To develop an adaptive decision-making tool for mobile robots, we propose a novel…