20 citations · 23 across the 4 of their papers we have counts for
6 papers
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Xuanlin Li, Brandon Trabucco, Dong Huk Park +4
The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generate…
Accelerating Quadratic Optimization with Reinforcement Learning
Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato +6
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapid…
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning
Ryan Hoque, Ashwin Balakrishna, Carl Putterman +6
Corrective interventions while a robot is learning to automate a task provide an intuitive method for a human supervisor to assist the robot and convey information about desired be…
Connecting Context-specific Adaptation in Humans to Meta-learning
Rachit Dubey, Erin Grant, Michael Luo +2
Cognitive control, the ability of a system to adapt to the demands of a task, is an integral part of cognition. A widely accepted fact about cognitive control is that it is context…
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
Eric Liang, Zhanghao Wu, Michael Luo +3
Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the…
IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
Michael Luo, Jiahao Yao, Richard Liaw +2
The practical usage of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinfor…