JMLR

Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights

Insung Kong, Yongdai Kim

Regularizing hard examples improves adversarial robustness

Hyungyu Lee, Saehyung Lee, Ho Bae, Sungroh Yoon

Sample-efficient Adversarial Imitation Learning

Dahuin Jung, Hyungyu Lee, Sungroh Yoon

A likelihood approach to nonparametric estimation of a singular distribution using deep generative models

Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin

Deep neural networks with dependent weights: Gaussian process mixture limit, heavy tails, sparsity and compressibility

Hoil Lee, Fadhel Ayed, Paul Jung, Juho Lee, Hongseok Yang, François Caron

Flexible model aggregation for quantile regression

Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J Smola, Ryan J Tibshirani

Minimal Width for Universal Property of Deep RNN

Chang hoon Song, Geonho Hwang, Jun ho Lee, and Myungjoo Kang