Yung-Kyun Noh

According to our database, Yung-Kyun Noh authored at least 40 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models.

Sangwoong Yoon, Himchan Hwang, Dohyun Kwon, Yung-Kyun Noh, Frank C. Park

CoRR, 2024

Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies.

Haanvid Lee, Tri Wahyu Guntara, Jongmin Lee, Yung-Kyun Noh, Kee-Eung Kim

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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, Kyong Hwan Jin, Giltae Song, Eun-Sol Kim, Jejoong Yoo, Eunok Paek, Yung-Kyun Noh, Keehyoung Joo

Bioinform., December, 2023

Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning.

Sangwoong Yoon, Dohyun Kwon, Himchan Hwang, Yung-Kyun Noh, Frank C. Park

CoRR, 2023

Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach.

Sangwoong Yoon, Young-Uk Jin, Yung-Kyun Noh, Frank C. Park

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Variational Weighting for Kernel Density Ratios.

Sangwoong Yoon, Frank C. Park, Gunsu S. Yun, Iljung Kim, Yung-Kyun Noh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Synchronization-Aware NAS for an Efficient Collaborative Inference on Mobile Platforms.

Beom Woo Kang, Junho Wohn, Seongju Lee, Sunghyun Park, Yung-Kyun Noh, Yongjun Park

Proceedings of the 24th ACM SIGPLAN/SIGBED International Conference on Languages, 2023

Geometrically regularized autoencoders for non-Euclidean data.

Cheongjae Jang, Yonghyeon Lee, Yung-Kyun Noh, Frank C. Park

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Nearest Neighbor Density Functional Estimation From Inverse Laplace Transform.

J. Jon Ryu, Shouvik Ganguly, Younghan Kim, Yung-Kyun Noh, Daniel D. Lee

IEEE Trans. Inf. Theory, 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

Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions.

Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon, Byung-Jun Lee, Yung-Kyun Noh, Kee-Eung Kim

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Reparametrization-Invariant Sharpness Measure Based on Information Geometry.

Cheongjae Jang, Sungyoon Lee, Frank C. Park, Yung-Kyun Noh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

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

A Riemannian geometric framework for manifold learning of non-Euclidean data.

Cheongjae Jang, Yung-Kyun Noh, Frank Chongwoo Park

Adv. Data Anal. Classif., 2021

Autoencoding Under Normalization Constraints.

Sangwoong Yoon, Yung-Kyun Noh, Frank Chongwoo Park

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Efficient neural network compression via transfer learning for machine vision inspection.

Seunghyeon Kim, Yung-Kyun Noh, Frank C. Park

Neurocomputing, 2020

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

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

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

Proceedings of the Conference on Research in Adaptive and Convergent Systems, 2019

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

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

Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence.

Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee

Neural Comput., 2018

K-Beam Subgradient Descent for Minimax Optimization.

Jihun Hamm, Yung-Kyun Noh

CoRR, 2018

Nearest neighbor density functional estimation based on inverse Laplace transform.

Shouvik Ganguly, Jongha Ryu, Young-Han Kim, Yung-Kyun Noh, Daniel D. Lee

CoRR, 2018

K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning.

Jihun Hamm, Yung-Kyun Noh

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Generative Local Metric Learning for Kernel Regression.

Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank C. Park, Daniel D. Lee

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Transfer learning for automated optical inspection.

Seunghyeon Kim, Wooyoung Kim, Yung-Kyun Noh, Frank Chongwoo Park

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Motion planning with movement primitives for cooperative aerial transportation in obstacle environment.

Hyoin Kim, Hyeonbeom Lee, Seungwon Choi, Yung-Kyun Noh, H. Jin Kim

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

2016

Direct Density Derivative Estimation.

Hiroaki Sasaki, Yung-Kyun Noh, Gang Niu, Masashi Sugiyama

Neural Comput., 2016

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 and Its Application in KL-Divergence Approximation.

Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Reward Shaping for Model-Based Bayesian Reinforcement Learning.

Hyeoneun Kim, Woosang Lim, Kanghoon Lee, Yung-Kyun Noh, Kee-Eung Kim

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Feature selection for brain-computer interface using nearest neighbor information.

Yung-Kyun Noh, Byoung-Kyong Min

Proceedings of the 2014 International Winter Workshop on Brain-Computer Interface, 2014

2013

k-Nearest Neighbor Classification Algorithm for Multiple Choice Sequential Sampling.

Yung-Kyun Noh, Frank Chongwoo Park, Daniel D. Lee

Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

2012

Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification.

Yung-Kyun Noh, Frank Chongwoo Park, Daniel D. Lee

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Learning Discriminative Metrics via Generative Models and Kernel Learning

Yuan Shi, Yung-Kyun Noh, Fei Sha, Daniel D. Lee

CoRR, 2011

2010

Fluid Dynamics Models for Low Rank Discriminant Analysis.

Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D. Lee

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

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

Regularized discriminant analysis for transformation-invariant object recognition.

Yung-Kyun Noh, Jihun Ham, Daniel D. Lee

Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008