Trivializations for Gradient-Based Optimization on Manifolds

1 code implementation • NeurIPS 2019 • Mario Lezcano Casado

We prove conditions under which a trivialization is sound in the context of gradient-based optimization and we show how two large families of trivializations have overall favorable properties, but also suffer from a performance issue.

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

no code implementations • 21 Dec 2017 • Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat

We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data.

Probabilistic Programming