1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
On-Policy Robot Imitation Learning from a Converging Supervisor
Ashwin Balakrishna, Brijen Thananjeyan, Jonathan Lee +4
Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor. However, there are many settings where the supervisor may evolve during polic…
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference
Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference. In the setting of graphical models, MAP inference entails solving a combinat…
Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models
Ajay Kumar Tanwani, Jonathan Lee, Brijen Thananjeyan +5
Generalizing manipulation skills to new situations requires extracting invariant patterns from demonstrations. For example, the robot needs to understand the demonstrations at a hi…