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ORCIDYung-Kyun Noh
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2020 – today
- 2024
[c22]Haanvid Lee, Tri Wahyu Guntara, Jongmin Lee, Yung-Kyun Noh, Kee-Eung Kim:
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies. ICLR 2024- [i12]Haanvid Lee, Tri Wahyu Guntara, Jongmin Lee, Yung-Kyun Noh, Kee-Eung Kim:
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies. CoRR abs/2405.18792 (2024) - [i11]Sangwoong Yoon, Himchan Hwang, Dohyun Kwon, Yung-Kyun Noh, Frank C. Park:
Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models. CoRR abs/2407.00626 (2024) - 2023
- [j13]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:
DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function. Bioinform. 39(12) (2023) - [c21]Cheongjae Jang, Yonghyeon Lee, Yung-Kyun Noh, Frank C. Park:
Geometrically regularized autoencoders for non-Euclidean data. ICLR 2023 - [c20]Beom Woo Kang, Junho Wohn, Seongju Lee, Sunghyun Park, Yung-Kyun Noh, Yongjun Park:
Synchronization-Aware NAS for an Efficient Collaborative Inference on Mobile Platforms. LCTES 2023: 13-25 - [c19]Sangwoong Yoon, Frank C. Park, Gunsu S. Yun, Iljung Kim, Yung-Kyun Noh:
Variational Weighting for Kernel Density Ratios. NeurIPS 2023 - [c18]Sangwoong Yoon, Young-Uk Jin, Yung-Kyun Noh, Frank C. Park:
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach. NeurIPS 2023 - [i10]Sangwoong Yoon, Young-Uk Jin, Yung-Kyun Noh, Frank C. Park:
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach. CoRR abs/2310.18677 (2023) - [i9]Sangwoong Yoon, Frank Chongwoo Park, Gunsu S. Yun, Iljung Kim, Yung-Kyun Noh:
Variational Weighting for Kernel Density Ratios. CoRR abs/2311.03001 (2023) - [i8]Sangwoong Yoon, Dohyun Kwon, Himchan Hwang, Yung-Kyun Noh, Frank C. Park:
Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning. CoRR abs/2312.03397 (2023) - 2022
- [j12]J. Jon Ryu, Shouvik Ganguly, Younghan Kim, Yung-Kyun Noh, Daniel D. Lee:
Nearest Neighbor Density Functional Estimation From Inverse Laplace Transform. IEEE Trans. Inf. Theory 68(6): 3511-3551 (2022) - [c17]Cheongjae Jang, Sungyoon Lee, Frank C. Park, Yung-Kyun Noh:
A Reparametrization-Invariant Sharpness Measure Based on Information Geometry. NeurIPS 2022 - [c16]Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon, Byung-Jun Lee, Yung-Kyun Noh, Kee-Eung Kim:
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions. NeurIPS 2022 - [i7]Sangwoong Yoon, Jinwon Choi, Yonghyeon Lee, Yung-Kyun Noh, Frank Chongwoo Park:
Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers. CoRR abs/2208.10940 (2022) - [i6]Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon, Byung-Jun Lee, Yung-Kyun Noh, Kee-Eung Kim:
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions. CoRR abs/2210.13373 (2022) - 2021
- [j11]Cheongjae Jang, Yung-Kyun Noh, Frank Chongwoo Park:
A Riemannian geometric framework for manifold learning of non-Euclidean data. Adv. Data Anal. Classif. 15(3): 673-699 (2021) - [j10]Sangseon Lee, Taeheon Lee, Yung-Kyun Noh, Sun Kim:
Ranked k-Spectrum Kernel for Comparative and Evolutionary Comparison of Exons, Introns, and CpG Islands. IEEE ACM Trans. Comput. Biol. Bioinform. 18(3): 1174-1183 (2021) - [c15]Sangwoong Yoon, Yung-Kyun Noh, Frank Chongwoo Park:
Autoencoding Under Normalization Constraints. ICML 2021: 12087-12097 - [i5]Cheongjae Jang, Sang-Kyun Ko, Yung-Kyun Noh, Jieun Choi, Jongwon Lim, Tae-Jeong Kim:
Learning to increase matching efficiency in identifying additional b-jets in the tt̅b̅ process. CoRR abs/2103.09129 (2021) - [i4]Sangwoong Yoon, Yung-Kyun Noh, Frank Chongwoo Park:
Autoencoding Under Normalization Constraints. CoRR abs/2105.05735 (2021) - 2020
- [j9]Seunghyeon Kim, Yung-Kyun Noh, Frank C. Park:
Efficient neural network compression via transfer learning for machine vision inspection. Neurocomputing 413: 294-304 (2020)
2010 – 2019
- 2019
- [j8]Yung-Kyun Noh, Ji Young Park, Byoung Geol Choi, Kee-Eung Kim, Seung-Woon Rha:
A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization. J. Medical Syst. 43(8): 253:1-253:8 (2019) - [j7]Masashi Sugiyama, Yung-Kyun Noh:
Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018). Mach. Learn. 108(5): 717-719 (2019) - [c14]Jiwon Hong, Sung-Jun Park, Taeri Kim, Yung-Kyun Noh, Sang-Wook Kim, Dongphil Kim, Wonho Kim:
Malware classification for identifying author groups: a graph-based approach. RACS 2019: 169-174 - 2018
- [j6]Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee:
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. Neural Comput. 30(7) (2018) - [j5]Yung-Kyun Noh, Jihun Hamm, Frank Chongwoo Park, Byoung-Tak Zhang, Daniel D. Lee:
Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 40(1): 92-105 (2018) - [j4]Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D. Lee:
Generative Local Metric Learning for Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Mach. Intell. 40(1): 106-118 (2018) - [c13]Jihun Hamm, Yung-Kyun Noh:
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning. ICML 2018: 1876-1884 - [i3]Shouvik Ganguly, Jongha Ryu, Young-Han Kim, Yung-Kyun Noh, Daniel D. Lee:
Nearest neighbor density functional estimation based on inverse Laplace transform. CoRR abs/1805.08342 (2018) - [i2]Jihun Hamm, Yung-Kyun Noh:
K-Beam Subgradient Descent for Minimax Optimization. CoRR abs/1805.11640 (2018) - 2017
- [c12]Hyoin Kim, Hyeonbeom Lee, Seungwon Choi, Yung-Kyun Noh, H. Jin Kim:
Motion planning with movement primitives for cooperative aerial transportation in obstacle environment. ICRA 2017: 2328-2334 - [c11]Seunghyeon Kim, Wooyoung Kim, Yung-Kyun Noh, Frank Chongwoo Park:
Transfer learning for automated optical inspection. IJCNN 2017: 2517-2524 - [c10]Yung-Kyun Noh, Masashi Sugiyama, Kee-Eung Kim, Frank C. Park, Daniel D. Lee:
Generative Local Metric Learning for Kernel Regression. NIPS 2017: 2452-2462 - [e1]Min-Ling Zhang, Yung-Kyun Noh:
Proceedings of The 9th Asian Conference on Machine Learning, ACML 2017, Seoul, Korea, November 15-17, 2017. Proceedings of Machine Learning Research 77, PMLR 2017 [contents] - 2016
- [j3]Hiroaki Sasaki, Yung-Kyun Noh, Gang Niu, Masashi Sugiyama:
Direct Density Derivative Estimation. Neural Comput. 28(6): 1101-1140 (2016) - 2015
- [j2]Yung-Kyun Noh, Daniel D. Lee, Kyung-Ae Yang, Cheong-Tag Kim, Byoung-Tak Zhang:
Molecular learning with DNA kernel machines. Biosyst. 137: 73-83 (2015) - [c9]Hyeoneun Kim, Woosang Lim, Kanghoon Lee, Yung-Kyun Noh, Kee-Eung Kim:
Reward Shaping for Model-Based Bayesian Reinforcement Learning. AAAI 2015: 3548-3555 - [c8]Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama:
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation. AISTATS 2015 - 2014
- [c7]Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee:
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. AISTATS 2014: 669-677 - [c6]Yung-Kyun Noh, Byoung-Kyong Min:
Feature selection for brain-computer interface using nearest neighbor information. BCI 2014: 1-3 - 2013
- [c5]Yung-Kyun Noh, Frank Chongwoo Park, Daniel D. Lee:
k-Nearest Neighbor Classification Algorithm for Multiple Choice Sequential Sampling. CogSci 2013 - 2012
- [c4]Yung-Kyun Noh, Frank Chongwoo Park, Daniel D. Lee:
Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification. NIPS 2012: 1934-1942 - 2011
- [i1]Yuan Shi, Yung-Kyun Noh, Fei Sha, Daniel D. Lee:
Learning Discriminative Metrics via Generative Models and Kernel Learning. CoRR abs/1109.3940 (2011) - 2010
- [c3]Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D. Lee:
Generative Local Metric Learning for Nearest Neighbor Classification. NIPS 2010: 1822-1830 - [c2]Yung-Kyun Noh, Byoung-Tak Zhang, Daniel D. Lee:
Fluid Dynamics Models for Low Rank Discriminant Analysis. AISTATS 2010: 565-572
2000 – 2009
- 2008
- [j1]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:
An evolutionary Monte Carlo algorithm for predicting DNA hybridization. Biosyst. 91(1): 69-75 (2008) - [c1]Yung-Kyun Noh, Jihun Ham, Daniel D. Lee:
Regularized discriminant analysis for transformation-invariant object recognition. ICPR 2008: 1-5
Coauthor Index
aka: Frank Chongwoo Park
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