Robust Inference via Generative Classifiers for Handling Noisy Labels

ICML 2019 Robust Inference via Generative Classifiers for Handling Noisy Labels Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin  Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern dee...

Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck

ICML 2019 Reliable Estimation of Individual Treatment Effect with Causal Information BottleneckSungyub Kim, Yongsu Baek, Sung Ju Hwang, Eunho Yang Estimating individual level treatment effects (ITE) from observational data is a challenging and important area in causal machine learning and ...

Learning What and Where to Transfer

ICML 2019 Learning What and Where to Transfer Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin  As the application of deep learning has expanded to real-world problems with insufficient volume of training data, transfer learning recently has gained much attention as means of ...

Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

ICLR 2019 Learning to Propagate Labels: Transductive Propagation Network for Few-shot LearningYanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a ...

InstaGAN: Instance-aware Image-to-Image Translation

ICLR 2019 InstaGAN: Instance-aware Image-to-ImageTranslation Sangwoo Mo, Minsu Cho, Jinwoo Shin  Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). However, previous meth...

Graph Embedding VAE: A Permutation Invariant Model of Graph Structure

NeurIPS 2019 Workshop on Graph Representation Learning Graph Embedding VAE: A Permutation Invariant Model of Graph StructureTony Duan and Juho Lee Generative models of graph structure have applications in biology and social sciences. The state of the art is GraphRNN, which decomposes ...

Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data

ACL 2019 Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming DataMoonsu Han, Minki Kang, Hyunwoo Jung, Sung Ju Hwang We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or v...

Deep Gaussian Processes for Weakly Supervised Learning: Tumor Mutation Burden (TMB) Prediction

NeurIPS 2019 Workshop on Bayesian Deep Learning  Deep Gaussian Processes for Weakly Supervised Learning: Tumor Mutation Burden (TMB) Prediction  Sunho Park, Hongming Xu, Tae Hyun Hwang, Saehoon Kim  Tumor mutation burden (TMB) is a quantitative measurement of ...

Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior

ICML 2019 (full oral presentation) Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior * Fadhel Ayed, * Juho Lee, François Caron (* indicates equal contribution)  Bayesian nonparametric approaches, in particular...

Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior

ICML 2019 (full oral presentation) Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior* Fadhel Ayed, * Juho Lee, François Caron (* indicates equal contribution) Bayesian nonparametric approaches, in particular the Pitman-Yor p...