127 citations · 278 across the 14 of their papers we have counts for
7 papers · 1 filter
Unsupervised Out-of-Distribution Detection with Batch Normalization
Jiaming Song, Yang Song, Stefano Ermon
Likelihood from a generative model is a natural statistic for detecting out-of-distribution (OoD) samples. However, generative models have been shown to assign higher likelihood to…
Bridging the Gap Between -GANs and Wasserstein GANs
Jiaming Song, Stefano Ermon
Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions. Learning objectives approximately minimize an -divergence (-GANs)…
Understanding the Limitations of Variational Mutual Information Estimators
Jiaming Song, Stefano Ermon
Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables. However, they can be difficult to use…
Domain Adaptive Imitation Learning
Kuno Kim, Yihong Gu, Jiaming Song +2
We study the question of how to imitate tasks across domains with discrepancies such as embodiment, viewpoint, and dynamics mismatch. Many prior works require paired, aligned demon…
Multi-Agent Adversarial Inverse Reinforcement Learning
Lantao Yu, Jiaming Song, Stefano Ermon
Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification. Finding a set of reward functions to properly guide agent behaviors is particularly…
Calibrated Model-Based Deep Reinforcement Learning
Ali Malik, Volodymyr Kuleshov, Jiaming Song +3
Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning. However, predictive uncertainties---especially ones derived from mod…