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Found 11 papers, 5 papers with code
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning
We propose a graphical model framework for goal-conditioned RL, with an EM algorithm that operates on the lower bound of the RL objective.
Variance Reduction for Evolution Strategies via Structured Control Variates
Evolution Strategies (ES) are a powerful class of blackbox optimization techniques that recently became a competitive alternative to state-of-the-art policy gradient (PG) algorithms for reinforcement learning (RL).
Variational Deep Q Network
We propose a framework that directly tackles the probability distribution of the value function parameters in Deep Q Network (DQN), with powerful variational inference subroutines to approximate the posterior of the parameters.
Evaluating Bayesian Models with Posterior Dispersion Indices
We propose to evaluate a model through posterior dispersion.
Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical information.
Robust Probabilistic Modeling with Bayesian Data Reweighting
We propose a way to systematically detect and mitigate mismatch of a large class of probabilistic models.
Variational Inference: A Review for Statisticians
One of the core problems of modern statistics is to approximate difficult-to-compute probability densities.
Automatic Variational Inference in Stan
With ADVI we can use variational inference on any model we write in Stan.
Population Empirical Bayes
We develop population empirical Bayes (POP-EB), a hierarchical framework that explicitly models the empirical population distribution as part of Bayesian analysis.
