Search Results for author: Yung-Kyun Noh

Found 15 papers, 5 papers with code

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

1 code implementation24 Oct 2022 Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon, Byung-Jun Lee, Yung-Kyun Noh, Kee-Eung Kim

We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces.

Metric Learning Multi-Armed Bandits +1

Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers

1 code implementation20 Aug 2022 Sangwoong Yoon, Jinwon Choi, Yonghyeon LEE, Yung-Kyun Noh, Frank Chongwoo Park

A reliable evaluation method is essential for building a robust out-of-distribution (OOD) detector.

Adversarial Distributions Against Out-of-Distribution Detectors

no code implementations29 Sep 2021 Sangwoong Yoon, Jinwon Choi, Yonghyeon LEE, Yung-Kyun Noh, Frank C. Park

As an outlier may deviate from the training distribution in unexpected ways, an ideal OOD detector should be able to detect all types of outliers.

Out of Distribution (OOD) Detection

Autoencoding Under Normalization Constraints

2 code implementations12 May 2021 Sangwoong Yoon, Yung-Kyun Noh, Frank Chongwoo Park

The specific role of the normalization constraint is to ensure that the out-of-distribution (OOD) regime has a small likelihood when samples are learned using maximum likelihood.

Outlier Detection

Learning to increase matching efficiency in identifying additional b-jets in the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process

no code implementations16 Mar 2021 Cheongjae Jang, Sang-Kyun Ko, Yung-Kyun Noh, Jieun Choi, Jongwon Lim, Tae Jeong Kim

The $\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process is an essential channel to reveal the Higgs properties but has an irreducible background from the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process, which produces a top quark pair in association with a b quark pair.


Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection

no code implementations1 Jan 2021 Sangwoong Yoon, Yung-Kyun Noh, Frank C. Park

This phenomenon, which we refer to as outlier reconstruction, has a detrimental effect on the use of autoencoders for outlier detection, as an autoencoder will misclassify a clear outlier as being in-distribution.

Outlier Detection Out-of-Distribution Detection

Efficient Neural Network Compression via Transfer Learning for Industrial Optical Inspection

no code implementations20 Oct 2018 Seunghyeon Kim, Yung-Kyun Noh, Frank C. Park

In this paper, we investigate learning the deep neural networks for automated optical inspection in industrial manufacturing.

Neural Network Compression Transfer Learning

K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning

1 code implementation ICML 2018 Jihun Hamm, Yung-Kyun Noh

Minimax optimization plays a key role in adversarial training of machine learning algorithms, such as learning generative models, domain adaptation, privacy preservation, and robust learning.

Domain Adaptation

Nearest neighbor density functional estimation from inverse Laplace transform

1 code implementation22 May 2018 J. Jon Ryu, Shouvik Ganguly, Young-Han Kim, Yung-Kyun Noh, Daniel D. Lee

A new approach to $L_2$-consistent estimation of a general density functional using $k$-nearest neighbor distances is proposed, where the functional under consideration is in the form of the expectation of some function $f$ of the densities at each point.

Domain Adaptation Using Adversarial Learning for Autonomous Navigation

no code implementations11 Dec 2017 Jaeyoon Yoo, Yongjun Hong, Yung-Kyun Noh, Sungroh Yoon

The objective of this study is to train an autonomous navigation model that uses a simulator (instead of real labeled data) and an inexpensive monocular camera.

Autonomous Navigation Domain Adaptation +1

Scalable Iterative Algorithm for Robust Subspace Clustering

no code implementations5 Mar 2015 Sanghyuk Chun, Yung-Kyun Noh, Jinwoo Shin

Subspace clustering (SC) is a popular method for dimensionality reduction of high-dimensional data, where it generalizes Principal Component Analysis (PCA).

Dimensionality Reduction

Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification

no code implementations NeurIPS 2012 Yung-Kyun Noh, Frank Park, Daniel D. Lee

This paper sheds light on some fundamental connections of the diffusion decision making model of neuroscience and cognitive psychology with k-nearest neighbor classification.

Classification Decision Making +1

Cannot find the paper you are looking for? You can Submit a new open access paper.