no code implementations • 17 May 2023 • Kenneth Bogert, Matthew Kothe
Previous remedies either relaxed feature constraints when accounting for observation error, given well-characterized errors such as zero-mean Gaussian, or chose to simply select the most likely model element given an observation.
no code implementations • 15 Aug 2022 • Kenneth Bogert, Yikang Gui, Prashant Doshi
We show that in generalizing the principle of maximum entropy to these types of scenarios we unavoidably introduce a dependency on the learned model to the empirical feature expectations.
no code implementations • 9 Sep 2021 • 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, for instance, feature expectations.
no code implementations • 13 Jul 2021 • Kenneth Bogert, Prashant Doshi
To address this, we present a hierarchical Bayesian model that incorporates both the expert's and the confounding elements' observations thereby explicitly modeling the diverse observations a robot may receive.
