Regularized discriminant analysis for transformation-invariant object recognition. - Yung-Kyun Noh; Jihun Ham; Daniel D. Lee (ICPR, 2008)

An evolutionary Monte Carlo algorithm for predicting DNA hybridization. - Joon Shik Kim; Ji-Woo Lee; Yung-Kyun Noh; Ji-Yoon Park; Dong-Yoon Lee; Kyung-Ae Yang; Young-Gyu Chai; Jong Chan Kim; Byoung-Tak Zhang (Biosyst., 2008)

Fluid Dynamics Models for Low Rank Discriminant Analysis. - Yung-Kyun Noh; Byoung-Tak Zhang; Daniel D. Lee (AISTATS, 2010)

Learning Discriminative Metrics via Generative Models and Kernel Learning - Yuan Shi; Yung-Kyun Noh; Fei Sha; Daniel D. Lee (CoRR, 2011)

Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification. - Yung-Kyun Noh; Frank Chongwoo Park; Daniel D. Lee (NIPS, 2012)

k-Nearest Neighbor Classification Algorithm for Multiple Choice Sequential Sampling. - Yung-Kyun Noh; Frank Chongwoo Park; Daniel D. Lee (CogSci, 2013)

Feature selection for brain-computer interface using nearest neighbor information. - Yung-Kyun Noh; Byoung-Kyong Min (BCI, 2014)

Reward Shaping for Model-Based Bayesian Reinforcement Learning. - Hyeoneun Kim; Woosang Lim; Kanghoon Lee; Yung-Kyun Noh; Kee-Eung Kim (AAAI, 2015)

Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation. - Hiroaki Sasaki; Yung-Kyun Noh; Masashi Sugiyama (AISTATS, 2015)

Molecular learning with DNA kernel machines. - Yung-Kyun Noh; Daniel D. Lee; Kyung-Ae Yang; Cheong-Tag Kim; Byoung-Tak Zhang (Biosyst., 2015)

Direct Density Derivative Estimation. - Hiroaki Sasaki; Yung-Kyun Noh; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2016)

Motion planning with movement primitives for cooperative aerial transportation in obstacle environment. - Hyoin Kim; Hyeonbeom Lee; Seungwon Choi; Yung-Kyun Noh; H. Jin Kim (ICRA, 2017)

Transfer learning for automated optical inspection. - Seunghyeon Kim; Wooyoung Kim; Yung-Kyun Noh; Frank Chongwoo Park (IJCNN, 2017)

Generative Local Metric Learning for Kernel Regression. - Yung-Kyun Noh; Masashi Sugiyama; Kee-Eung Kim; Frank C. Park 001; Daniel D. Lee (NIPS, 2017)

K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning. - Jihun Hamm; Yung-Kyun Noh (ICML, 2018)

Nearest neighbor density functional estimation based on inverse Laplace transform. - Shouvik Ganguly; Jongha Ryu; Young-Han Kim 001; Yung-Kyun Noh; Daniel D. Lee (CoRR, 2018)

K-Beam Subgradient Descent for Minimax Optimization. - Jihun Hamm; Yung-Kyun Noh (CoRR, 2018)

Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. - Yung-Kyun Noh; Masashi Sugiyama; Song Liu 002; Marthinus Christoffel du Plessis; Frank Chongwoo Park; Daniel D. Lee (Neural Comput., 2018)

Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis. - Yung-Kyun Noh; Jihun Hamm; Frank Chongwoo Park; Byoung-Tak Zhang; Daniel D. Lee (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

Generative Local Metric Learning for Nearest Neighbor Classification. - Yung-Kyun Noh; Byoung-Tak Zhang; Daniel D. Lee (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

Malware classification for identifying author groups: a graph-based approach. - Jiwon Hong; Sung-Jun Park; Taeri Kim; Yung-Kyun Noh; Sang-Wook Kim; Dongphil Kim; Wonho Kim (RACS, 2019)

A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization. - Yung-Kyun Noh; Ji Young Park; Byoung Geol Choi; Kee-Eung Kim; Seung-Woon Rha (J. Medical Syst., 2019)

Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018). - Masashi Sugiyama; Yung-Kyun Noh (Mach. Learn., 2019)

Efficient neural network compression via transfer learning for machine vision inspection. - Seunghyeon Kim; Yung-Kyun Noh; Frank C. Park 001 (Neurocomputing, 2020)

Autoencoding Under Normalization Constraints. - Sangwoong Yoon; Yung-Kyun Noh; Frank Chongwoo Park (ICML, 2021)

A Riemannian geometric framework for manifold learning of non-Euclidean data. - Cheongjae Jang; Yung-Kyun Noh; Frank Chongwoo Park (Adv. Data Anal. Classif., 2021)

Learning to increase matching efficiency in identifying additional b-jets in the tt̅b̅ process. - Cheongjae Jang; Sang-Kyun Ko; Yung-Kyun Noh; Jieun Choi; Jongwon Lim; Tae-Jeong Kim (CoRR, 2021)

Ranked k-Spectrum Kernel for Comparative and Evolutionary Comparison of Exons, Introns, and CpG Islands. - Sangseon Lee; Taeheon Lee; Yung-Kyun Noh; Sun Kim (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

A Reparametrization-Invariant Sharpness Measure Based on Information Geometry. - Cheongjae Jang; Sungyoon Lee; Frank C. Park 001; Yung-Kyun Noh (NeurIPS, 2022)

Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions. - Haanvid Lee; Jongmin Lee 004; Yunseon Choi; Wonseok Jeon; Byung-Jun Lee 001; Yung-Kyun Noh; Kee-Eung Kim (NeurIPS, 2022)

Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers. - Sangwoong Yoon; Jinwon Choi; Yonghyeon Lee; Yung-Kyun Noh; Frank Chongwoo Park (CoRR, 2022)

Nearest Neighbor Density Functional Estimation From Inverse Laplace Transform. - J. Jon Ryu; Shouvik Ganguly; Younghan Kim 001; Yung-Kyun Noh; Daniel D. Lee (IEEE Trans. Inf. Theory, 2022)

Geometrically regularized autoencoders for non-Euclidean data. - Cheongjae Jang; Yonghyeon Lee; Yung-Kyun Noh; Frank C. Park 001 (ICLR, 2023)

Synchronization-Aware NAS for an Efficient Collaborative Inference on Mobile Platforms. - Beom Woo Kang; Junho Wohn; Seongju Lee; Sunghyun Park 004; Yung-Kyun Noh; Yongjun Park 001 (LCTES, 2023)

Variational Weighting for Kernel Density Ratios. - Sangwoong Yoon; Frank C. Park 001; Gunsu S. Yun; Iljung Kim; Yung-Kyun Noh (NeurIPS, 2023)

Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach. - Sangwoong Yoon; Young-Uk Jin; Yung-Kyun Noh; Frank C. Park 001 (NeurIPS, 2023)

Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning. - Sangwoong Yoon; Dohyun Kwon 002; Himchan Hwang; Yung-Kyun Noh; Frank C. Park 001 (CoRR, 2023)

DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function. - Jae-Won Lee; Jong-Hyun Won; Seonggwang Jeon; Yujin Choo; Yubin Yeon; Jin-Seon Oh; Minsoo Kim; Seonhwa Kim; InSuk Joung; Cheongjae Jang; Sung Jong Lee; Tae Hyun Kim; et al. (Bioinform., 2023)

Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies. - Haanvid Lee; Tri Wahyu Guntara; Jongmin Lee 004; Yung-Kyun Noh; Kee-Eung Kim (ICLR, 2024)

Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models. - Sangwoong Yoon; Himchan Hwang; Dohyun Kwon 002; Yung-Kyun Noh; Frank C. Park 001 (NeurIPS, 2024)