223 citations · 250 across the 4 of their papers we have counts for
7 papers
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale
Kuang-Huei Lee, Ted Xiao, Adrian Li +3
The predictive information, the mutual information between the past and future, has been shown to be a useful representation learning auxiliary loss for training reinforcement lear…
An Empirical Investigation of Representation Learning for Imitation
Xin Chen, Sam Toyer, Cody Wild +9
Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might find itself in during deployment. However, collecting e…
CEB Improves Model Robustness
Ian Fischer, Alexander A. Alemi
We demonstrate that the Conditional Entropy Bottleneck (CEB) can improve model robustness. CEB is an easy strategy to implement and works in tandem with data augmentation procedure…
Phase Transitions for the Information Bottleneck in Representation Learning
Tailin Wu, Ian Fischer
In the Information Bottleneck (IB), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what's their relationship with the…
Learning Latent Dynamics for Planning from Pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer +4
Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from intera…
Uncertainty in the Variational Information Bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon
We present a simple case study, demonstrating that Variational Information Bottleneck (VIB) can improve a network's classification calibration as well as its ability to detect out-…