Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. - Mario Lezcano Casado; Atilim Gunes Baydin; David Martínez-Rubio; Tuan Anh Le 001; Frank D. Wood; Lukas Heinrich; Gilles Louppe; Kyle Cranmer; Karen Ng; Wahid Bhimji; Prabhat (CoRR, 2017)

Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group. - Mario Lezcano Casado; David Martínez-Rubio (ICML, 2019)

Trivializations for Gradient-Based Optimization on Manifolds. - Mario Lezcano Casado (NeurIPS, 2019)

Curvature-Dependant Global Convergence Rates for Optimization on Manifolds of Bounded Geometry. - Mario Lezcano Casado (CoRR, 2020)

Adaptive and Momentum Methods on Manifolds Through Trivializations. - Mario Lezcano Casado (CoRR, 2020)

Geometric Optimisation on Manifolds with Applications to Deep Learning. - Mario Lezcano Casado (CoRR, 2022)

Automatic Differentiation: Theory and Practice. - Mario Lezcano Casado (CoRR, 2022)