4 citations · 15 across the 9 of their papers we have counts for
9 papers
Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow
Seonho Park, Pascal Van Hentenryck
Security-Constrained Optimal Power Flow (SCOPF) plays a crucial role in power grid stability but becomes increasingly complex as systems grow. This paper introduces PDL-SCOPF, a se…
Compact Optimization Learning for AC Optimal Power Flow
Seonho Park, Wenbo Chen, Terrence W. K. Mak +1
This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability is…
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Seonho Park, Wenbo Chen, Dahye Han +2
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased…
Self-Supervised Primal-Dual Learning for Constrained Optimization
Seonho Park, Pascal Van Hentenryck
This paper studies how to train machine-learning models that directly approximate the optimal solutions of constrained optimization problems. This is an empirical risk minimization…
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
Wenbo Chen, Seonho Park, Mathieu Tanneau +1
The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring relia…
Homography augumented momentum constrastive learning for SAR image retrieval
Seonho Park, Maciej Rysz, Kathleen M. Dipple +1
Deep learning-based image retrieval has been emphasized in computer vision. Representation embedding extracted by deep neural networks (DNNs) not only aims at containing semantic i…
Deep Data Density Estimation through Donsker-Varadhan Representation
Seonho Park, Panos M. Pardalos
Estimating the data density is one of the challenging problems in deep learning. In this paper, we present a simple yet effective method for estimating the data density using a dee…
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection
Seonho Park, George Adosoglou, Panos M. Pardalos
Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstructi…
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization
Seonho Park, Seung Hyun Jung, Panos M. Pardalos
We focus on minimizing nonconvex finite-sum functions that typically arise in machine learning problems. In an attempt to solve this problem, the adaptive cubic regularized Newton…