26 citations · 41 across the 4 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.…
Detecting Spiky Corruption in Markov Decision Processes
Jason Mancuso, Tomasz Kisielewski, David Lindner +1
Current reinforcement learning methods fail if the reward function is imperfect, i.e. if the agent observes reward different from what it actually receives. We study this problem w…