1 code implementation • 5 Feb 2022 • J. Jon Ryu, Young-Han Kim
Recently, Qiao, Duan, and Cheng~(2019) proposed a distributed nearest-neighbor classification method, in which a massive dataset is split into smaller groups, each processed with a $k$-nearest-neighbor classifier, and the final class label is predicted by a majority vote among these groupwise class labels.
1 code implementation • 4 Feb 2022 • J. Jon Ryu, Alankrita Bhatt, Young-Han Kim
A class of parameter-free online linear optimization algorithms is proposed that harnesses the structure of an adversarial sequence by adapting to some side information.
no code implementations • 25 Sep 2019 • J. Jon Ryu, Yoojin Choi, Young-Han Kim, Mostafa El-Khamy, Jungwon Lee
A new variational autoencoder (VAE) model is proposed that learns a succinct common representation of two correlated data variables for conditional and joint generation tasks.
no code implementations • 27 May 2019 • J. Jon Ryu, Yoojin Choi, Young-Han Kim, Mostafa El-Khamy, Jungwon Lee
A new bimodal generative model is proposed for generating conditional and joint samples, accompanied with a training method with learning a succinct bottleneck representation.
1 code implementation • 22 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.
no code implementations • 28 Jun 2017 • Jaeyoon Yoo, Heonseok Ha, Jihun Yi, Jongha Ryu, Chanju Kim, Jung-Woo Ha, Young-Han Kim, Sungroh Yoon
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user.
1 code implementation • 16 Nov 2015 • Sunyoung Kwon, Gyuwan Kim, Byunghan Lee, Jongsik Chun, Sungroh Yoon, Young-Han Kim
Motivated by the need for fast and accurate classification of unlabeled nucleotide sequences on a large scale, we developed NASCUP, a new classification method that captures statistical structures of nucleotide sequences by compact context-tree models and universal probability from information theory.
Genomics Information Theory Information Theory
3 code implementations • 11 Jan 2012 • Jiantao Jiao, Haim H. Permuter, Lei Zhao, Young-Han Kim, Tsachy Weissman
Four estimators of the directed information rate between a pair of jointly stationary ergodic finite-alphabet processes are proposed, based on universal probability assignments.
Information Theory Information Theory