Bayesian Inference
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees (with Supplementary Material)
Supplementary material is at the end of this document.
Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees
V ariable selection is often of fundamental interest in many data science applications, providing benefits in prediction error, interpretability, and computation by excluding unnecessary variables.
Collapsed Variational Bounds for Bayesian Neural Networks
In this paper, we also apply inference to the prior parameters of Gaussian BNNs (e.g.
Supplementary Material: Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging Summary of the proposed algorithms and derived update rules
Choose a random initial value for p as well as h, and construct H = diag( h) and P = diag( p) .
Distributionally Robust Imitation Learning
Decision Process (MDP) setting where the reward function is not given, but demonstrations from experts are available. Although the goal of imitation learning is to learn a policy that produces behaviors nearly as good as the experts' for a desired task, assumptions of consistent optimality for demonstrated behaviors are often violated in practice.