138 citations · 397 across the 26 of their papers we have counts for
6 papers · 1 filter
A Unified Algorithm for Stochastic Path Problems
Christoph Dann, Chen-Yu Wei, Julian Zimmert
We study reinforcement learning in stochastic path (SP) problems. The goal in these problems is to maximize the expected sum of rewards until the agent reaches a terminal state. We…
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
Christoph Dann, Mehryar Mohri, Tong Zhang +1
Thompson Sampling is one of the most effective methods for contextual bandits and has been generalized to posterior sampling for certain MDP settings. However, existing posterior s…
Best of Both Worlds Model Selection
Aldo Pacchiano, Christoph Dann, Claudio Gentile
We study the problem of model selection in bandit scenarios in the presence of nested policy classes, with the goal of obtaining simultaneous adversarial and stochastic ("best of b…
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Christoph Dann, Yishay Mansour, Mehryar Mohri +2
Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian noise fail to explore efficiently in some reinforcement learning tasks and yet, they perform well in many o…
Leveraging Initial Hints for Free in Stochastic Linear Bandits
Ashok Cutkosky, Chris Dann, Abhimanyu Das +2
We study the setting of optimizing with bandit feedback with additional prior knowledge provided to the learner in the form of an initial hint of the optimal action. We present a n…
Same Cause; Different Effects in the Brain
Mariya Toneva, Jennifer Williams, Anand Bollu +2
To study information processing in the brain, neuroscientists manipulate experimental stimuli while recording participant brain activity. They can then use encoding models to find…