Learning Graphical Models
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent Priyank Jaini Bosch-Delta Lab University of Amsterdam Lars Holdijk
We first introduce Equivari-ant Stein V ariational Gradient Descent algorithm - an equivariant sampling method based on Stein's identity for sampling from densities with symmetries.
Bayesian Risk Markov Decision Processes
Markov decision process (MDP) is a paradigm for modeling sequential decision making under uncertainty. From a modeling perspective, some parameters of MDPs are unknown and need to be estimated from data.
Uncertainty-Driven Loss for Single Image Super-Resolution
How to achieve such spatial adaptation in a principled manner has been an open problem in both traditional model-based and modern learning-based approaches toward SISR. In this paper, we propose a new adaptive weighted loss for SISR to train deep networks focusing on challenging situations such as textured and edge pixels with high uncertainty.
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
We study the role of the representation of state-action value functions in regret minimization in finite-horizon Markov Decision Processes (MDPs) with linear structure.
A Variational Approach for Learning from Positive and Unlabeled Data Hui Chen
However, such a method easily leads to severe overfitting.
