1 citations · 2 across the 4 of their papers we have counts for
4 papers
The Principle of Uncertain Maximum Entropy
Kenneth Bogert, Matthew Kothe
The Principle of Maximum Entropy is a rigorous technique for estimating an unknown distribution given partial information while simultaneously minimizing bias. However, an importan…
IRL with Partial Observations using the Principle of Uncertain Maximum Entropy
Kenneth Bogert, Yikang Gui, Prashant Doshi
The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible while constrained to match empirically…
Notes on Generalizing the Maximum Entropy Principle to Uncertain Data
Kenneth Bogert
The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible constrained to match empirical data, f…
A Hierarchical Bayesian model for Inverse RL in Partially-Controlled Environments
Kenneth Bogert, Prashant Doshi
Robots learning from observations in the real world using inverse reinforcement learning (IRL) may encounter objects or agents in the environment, other than the expert, that cause…