Shallow Neural Network can Perfectly Classify an Object following Separable Probability Distribution. - Youngjae Min; Hye Won Chung (ISIT, 2019)

A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking. - Soon-Seo Park; Youngjae Min; Jung-Su Ha; Doo-Hyun Cho; Han-Lim Choi (IEEE Access, 2019)

Informative Planning of Mobile Sensor Networks in GPS-Denied Environments. - Youngjae Min; Soon-Seo Park; Han-Lim Choi (CoRR, 2019)

Kernel-Based 3-D Dynamic Occupancy Mapping with Particle Tracking. - Youngjae Min; Do-Un Kim; Han-Lim Choi (ICRA, 2021)

One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares. - Youngjae Min; Kwangjun Ahn; Navid Azizan (CDC, 2022)

Data-Driven Control with Inherent Lyapunov Stability. - Youngjae Min; Spencer M. Richards; Navid Azizan (CDC, 2023)

DS-K3DOM: 3-D Dynamic Occupancy Mapping with Kernel Inference and Dempster-Shafer Evidential Theory. - Juyeop Han; Youngjae Min; Hyeok-Joo Chae; Byeong-Min Jeong; Han-Lim Choi (ICRA, 2023)

SketchOGD: Memory-Efficient Continual Learning. - Benjamin Wright; Youngjae Min; Jeremy Bernstein; Navid Azizan (CoRR, 2023)

Π-ORFit: One-Pass Learning with Bregman Projection. - Namhoon Cho; Youngjae Min; Hyo-Sang Shin; Navid Azizan (ACC, 2024)

Hard-Constrained Neural Networks with Universal Approximation Guarantees. - Youngjae Min; Anoopkumar Sonar; Navid Azizan (CoRR, 2024)