Meta Variance Transfer: Learning to Augment from The Others

ICML 2020 Meta Variance Transfer: Learning to Augment from The Others SeongJin Park, Seungju Han, Jiwon Baek, Insoo Kim, Juhwan Song, Hae Beom Lee, Jae-Joon Han, Sung Ju Hwang  Humans have the ability to robustly recognize objects with various factors of variations such as n...

Meta Dropout: Learning to Perturb Latent Features for Generalization

ICLR 2020 Meta Dropout: Learning to Perturb Latent Features for Generalization Hae Beom Lee, Taewook Nam, Eunho Yang, Sung Ju Hwang  A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we know how to optimally perturb tra...

Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction

NeurIPS 2020 Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction Jinheon Baek, Dong Bok Lee, Sung Ju Hwang  Many practical graph problems, such as knowledge graph construction and drug-to-drug interaction, require to handle multi-relati...

Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks

ICLR 2020 Learning to Balance: Bayesian Meta-Learning forImbalanced and Out-of-distribution Tasks Hae Beom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang, Sung Ju Hwang  While tasks could come with varying the number of instances and classes in realistic sett...

Federated Semi-Supervised Learning with Inter-Client Consistency

ICML 2020 (Workshop in Federated Learning) Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning Wonyong Jeong, Jaehong Yoon, Eunho Yang, Sung Ju Hwang  While existing federated learning approaches mostly require that clients have fully-labeled ...

Federated Continual Learning with Weighted Inter-client Transfer

ICML 2021 Federated Continual Learning with Weighted Inter-client Transfer Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang and Sung Ju Hwang  There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks ...

Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

NeurIPS 2020 Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, Jinwoo Shin  While semi-supervised learning (SSL) has proven to be a promising way for leveraging unlabeled ...

Deep Mixed Effect Models using Gaussian Process: A Personalized and Reliable Prediction Model for Healthcare

AAAI 2020 Deep Mixed Effect Models using Gaussian Process: A Personalized and Reliable Prediction Model for Healthcare Ingyo Chung, Saehoon Kim, Juho Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang  We present a personalized and reliable prediction model for healthcare, which can ...

Cost-effective Interactive Attention Learning with Neural Attention Processes

ICML 2020 Cost-effective Interactive Attention with Neural Attention Processes Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang  We propose a novel interactive learning framework which we refer to as Interactive Attention Learning (IA...

Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks

ACS 2020 Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks Doyeong Hwang, Soojung Yang, Yongchan Kwon, Kyung Hoon Lee, Grace Lee, Hanseok Jo, Seyeol Yoon, Seongok Ryu  This work considers strategies to develop accurate and reliable gra...