Bayesian Inference
De-randomizing MCMC dynamics with the diffusion Stein operator
Approximate Bayesian inference estimates descriptors of an intractable target distribution - in essence, an optimization problem within a family of distributions.
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
While Bayesian neural networks (BNNs) hold the promise of being flexible, well-calibrated statistical models, inference often requires approximations whose consequences are poorly understood.
Dynamic Bottleneck for Robust Self-Supervised Exploration
However, such methods are usually sensitive to environmental dynamics-irrelevant information, e.g., white-noise. To handle such dynamics-irrelevant information, we propose a Dynamic Bottleneck (DB) model, which attains a dynamics-relevant representation based on the information-bottleneck principle.
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting
However, most systems of practical interest are non-linear, requiring more complex models. Many approximate inference methods have been developed for non-linear dynamical systems: Deterministic methods approximate the filtering and smoothing distributions e.g. by using a Taylor series
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.
