13 citations · 19 across the 6 of their papers we have counts for
6 papers
A Machine Learning Approach to Two-Stage Adaptive Robust Optimization
Dimitris Bertsimas, Cheol Woo Kim
We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty…
Optimal Control of Multiclass Fluid Queueing Networks: A Machine Learning Approach
Dimitris Bertsimas, Cheol Woo Kim
We propose a machine learning approach to the optimal control of multiclass fluid queueing networks (MFQNETs) that provides explicit and insightful control policies. We prove that…
The Deep Minimizing Movement Scheme
Min Sue Park, Cheolhyeong Kim, Hwijae Son +1
Solutions of certain partial differential equations (PDEs) are often represented by the steepest descent curves of corresponding functionals. Minimizing movement scheme was develop…
Prior Preference Learning from Experts:Designing a Reward with Active Inference
Jin young Shin, Cheolhyeong Kim, Hyung Ju Hwang
Active inference may be defined as Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior…
NEAR: Neighborhood Edge AggregatoR for Graph Classification
Cheolhyeong Kim, Haeseong Moon, Hyung Ju Hwang
Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an important field because of its wide applicability in bioinformatics, chemoinformat…
Local Stability and Performance of Simple Gradient Penalty mu-Wasserstein GAN
Cheolhyeong Kim, Seungtae Park, Hyung Ju Hwang
Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution. Recent studies have proposed stabilizing the training…