← Authors

Yung-kyun Noh

15 papers · 2010–2026 · 5 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (7) πŸ—ΊοΈ Taxonomy Completionist (38) 🧭 Keyword Pioneer 🌍 Conference Polyglot (4) πŸƒ Academic Marathon (14) πŸ† Grand Slam πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (63) πŸ’Ž Century Club (14)

Conferences

NIPS (8) AISTATS (2) ICLR (2) ICML (2) AAAI (1)

Top co-authors

Frank Park (6) Sangwoong Yoon (4) Daniel D. Lee (4) Masashi Sugiyama (3) Kee-eung Kim (3) Cheongjae Jang (3) Haanvid Lee (2) Jongmin Lee (2) Frank C. Park (2) Tri Wahyu Guntara (1)

Keywords

metric learning (4) energy-based model (3) bias reduction (2) nearest neighbor classification (2) diffusion model (2) nearest neighbor (2) generative model (2) off-policy evaluation (1) adversarial learning (1) neural decoding (1) change detection (1) density estimation (1) feature selection (1) kl divergence (1) anomaly detection (1) maximum likelihood (1) domain adaptation (1) outlier detection (1) inverse reinforcement learning (1) maximum entropy (1)

Papers

On the Information Processing of One-Dimensional Wasserstein Distances with Finite Samples AAAI 2026
Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models NIPS 2024
Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies ICLR 2024
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach NIPS 2023
Variational Weighting for Kernel Density Ratios NIPS 2023
Geometrically regularized autoencoders for non-Euclidean data ICLR 2023
Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions NIPS 2022
A Reparametrization-Invariant Sharpness Measure Based on Information Geometry NIPS 2022
Autoencoding Under Normalization Constraints ICML 2021
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning ICML 2018
Generative Local Metric Learning for Kernel Regression NIPS 2017
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation AISTATS 2015
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence AISTATS 2014
Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification NIPS 2012
Generative Local Metric Learning for Nearest Neighbor Classification NIPS 2010