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cs.LG2019
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…
cs.LG2019
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…