138 citations · 397 across the 26 of their papers we have counts for
3 papers · 1 filter
Decoupling Learning Rules from Representations
Philip S. Thomas, Christoph Dann, Emma Brunskill
In the artificial intelligence field, learning often corresponds to changing the parameters of a parameterized function. A learning rule is an algorithm or mathematical expression…
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann, Tor Lattimore, Emma Brunskill
Statistical performance bounds for reinforcement learning (RL) algorithms can be critical for high-stakes applications like healthcare. This paper introduces a new framework for th…
Sample Efficient Policy Search for Optimal Stopping Domains
Karan Goel, Christoph Dann, Emma Brunskill
Optimal stopping problems consider the question of deciding when to stop an observation-generating process in order to maximize a return. We examine the problem of simultaneously l…