56 citations · 266 across the 35 of their papers we have counts for
5 papers · 1 filter
Measuring Data
Margaret Mitchell, Alexandra Sasha Luccioni, Nathan Lambert +7
We identify the task of measuring data to quantitatively characterize the composition of machine learning data and datasets. Similar to an object's height, width, and volume, data…
Reward Reports for Reinforcement Learning
Thomas Krendl Gilbert, Nathan Lambert, Sarah Dean +2
Building systems that are good for society in the face of complex societal effects requires a dynamic approach. Recent approaches to machine learning (ML) documentation have demons…
Investigating Compounding Prediction Errors in Learned Dynamics Models
Nathan Lambert, Kristofer Pister, Roberto Calandra
Accurately predicting the consequences of agents' actions is a key prerequisite for planning in robotic control. Model-based reinforcement learning (MBRL) is one paradigm which rel…
The Challenges of Exploration for Offline Reinforcement Learning
Nathan Lambert, Markus Wulfmeier, William Whitney +5
Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked processes of reinforcement learning: collecting informative experience and inferring optimal…
Choices, Risks, and Reward Reports: Charting Public Policy for Reinforcement Learning Systems
Thomas Krendl Gilbert, Sarah Dean, Tom Zick +1
In the long term, reinforcement learning (RL) is considered by many AI theorists to be the most promising path to artificial general intelligence. This places RL practitioners in a…