95 citations · 169 across the 14 of their papers we have counts for
4 papers · 1 filter
Addressing the Long-term Impact of ML Decisions via Policy Regret
David Lindner, Hoda Heidari, Andreas Krause
Machine Learning (ML) increasingly informs the allocation of opportunities to individuals and communities in areas such as lending, education, employment, and beyond. Such decision…
Learning What To Do by Simulating the Past
David Lindner, Rohin Shah, Pieter Abbeel +1
Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such…
Challenges for Using Impact Regularizers to Avoid Negative Side Effects
David Lindner, Kyle Matoba, Alexander Meulemans
Designing reward functions for reinforcement learning is difficult: besides specifying which behavior is rewarded for a task, the reward also has to discourage undesired outcomes.…
Information Directed Reward Learning for Reinforcement Learning
David Lindner, Matteo Turchetta, Sebastian Tschiatschek +2
For many reinforcement learning (RL) applications, specifying a reward is difficult. This paper considers an RL setting where the agent obtains information about the reward only by…