Deep Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare

arXiv 2018 Deep Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare Ingyo Chung, Saehoon Kim, Juho Lee, Sung Ju Hwang, Eunho Yang  We present a personalized and reliable prediction model for healthcare, which can provide individually tailore...

Trimming the ℓ 1 Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning

ICML 2019 (full oral presentation) Trimming the ℓ 1 Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning Jihun Yun, Peng Zheng, Aurelie Lozano, Aleksandr Aravkin, Eunho Yang  We study high-dimensional estimators with the trimmed ℓ1 penalty,...

Training CNNs with Selective Allocation of Channels

ICML 2019 Training CNNs with Selective Allocation ofChannels Jongheon Jeong, Jinwoo Shin  Recent progress in deep convolutional neural networks (CNNs) have enabled a simple paradigm of architecture design: larger models typically achieve better accuracy. Due to this, in modern CN...

Towards Deep Amortized Clustering

NeurIPS 2019 Workshop on Sets & Partitions Towards Deep Amortized ClusteringJuho Lee, Yoonho Lee, Yee Whye Teh We tackle amortized clustering, the problem of learning a neural network that can cluster a new dataset with only a few forward passes. We propose a novel learning framework f...

Stochastic Gradient Methods with Block Diagonal Matrix Adaptation

ICML 2019 Stochastic Gradient Methods with Block Diagonal Matrix Adaptation Jihun Yun, Aurelie C. Lozano, Eunho Yang  Adaptive gradient approaches that automatically adjust the learning rate on a per-feature basis have been very popular for training deep networks. This rich class...

Spectral Approximate Inference

ICML 2019 Spectral Approximate Inference Sejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin  Given a graphical model (GM), computing its partition function is the most essential inference task, but it is computationally intractable in general. To address the issue, iterative ...

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

ICML 2019 Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh  Many machine learning tasks such as multiple instance learning, 3D shape recognition, and ...

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 ...