3 citations · 9 across the 6 of their papers we have counts for
3 papers · 1 filter
Online Model Selection for Reinforcement Learning with Function Approximation
Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar +2
Deep reinforcement learning has achieved impressive successes yet often requires a very large amount of interaction data. This result is perhaps unsurprising, as using complicated…
Is Q-Learning Provably Efficient? An Extended Analysis
Kushagra Rastogi, Jonathan Lee, Fabrice Harel-Canada +1
This work extends the analysis of the theoretical results presented within the paper Is Q-Learning Provably Efficient? by Jin et al. We include a survey of related research to cont…
Accelerated Message Passing for Entropy-Regularized MAP Inference
Jonathan N. Lee, Aldo Pacchiano, Peter Bartlett +1
Maximum a posteriori (MAP) inference in discrete-valued Markov random fields is a fundamental problem in machine learning that involves identifying the most likely configuration of…