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)

Online Learning Rate Adaptation with Hypergradient Descent. - Atilim Gunes Baydin; Robert Cornish; David Martínez-Rubio; Mark Schmidt 001; Frank Wood (ICLR (Poster), 2018)

Decentralized Cooperative Stochastic Multi-armed Bandits. - David Martínez-Rubio; Varun Kanade; Patrick Rebeschini (CoRR, 2018)

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

Decentralized Cooperative Stochastic Bandits. - David Martínez-Rubio; Varun Kanade; Patrick Rebeschini (NeurIPS, 2019)

Neural networks are a priori biased towards Boolean functions with low entropy. - Chris Mingard; Joar Skalse; Guillermo Valle Pérez; David Martínez-Rubio; Vladimir Mikulik; Ard A. Louis (CoRR, 2019)

Global Riemannian Acceleration in Hyperbolic and Spherical Spaces. - David Martínez-Rubio (ALT, 2022)

Fast Algorithms for Packing Proportional Fairness and its Dual. - Francisco Criado; David Martínez-Rubio; Sebastian Pokutta (NeurIPS, 2022)

Open Problem: Polynomial linearly-convergent method for g-convex optimization? - Christopher Criscitiello; David Martínez-Rubio; Nicolas Boumal (COLT, 2023)

Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. - David Martínez-Rubio; Sebastian Pokutta (COLT, 2023)

Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. - David Martínez-Rubio; Elias Samuel Wirth; Sebastian Pokutta (COLT, 2023)

Open Problem: Polynomial linearly-convergent method for geodesically convex optimization? - Christopher Criscitiello; David Martínez-Rubio; Nicolas Boumal (CoRR, 2023)

Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point. - David Martínez-Rubio; Christophe Roux; Sebastian Pokutta (ICML, 2024)

Non-Euclidean High-Order Smooth Convex Optimization. - Juan Pablo Contreras; Cristóbal Guzmán; David Martínez-Rubio (CoRR, 2024)

Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. - David Martínez-Rubio; Christophe Roux; Christopher Criscitiello; Sebastian Pokutta (AISTATS, 2025)

Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization. - Simon Vary; David Martínez-Rubio; Patrick Rebeschini (AISTATS, 2025)

Non-Euclidean High-Order Smooth Convex Optimization Extended Abstract. - Juan Pablo Contreras; Cristóbal Guzmán; David Martínez-Rubio (COLT, 2025)

Implicit Riemannian Optimism with Applications to Min-Max Problems. - Christophe Roux; David Martínez-Rubio; Sebastian Pokutta (CoRR, 2025)

Beyond Short Steps in Frank-Wolfe Algorithms. - David Martínez-Rubio; Sebastian Pokutta (CoRR, 2025)

Linear Convergence of the Frank-Wolfe Algorithm over Product Polytopes. - Gabriele Iommazzo; David Martínez-Rubio; Francisco Criado; Elias Samuel Wirth; Sebastian Pokutta (CoRR, 2025)

On the necessity of adaptive regularisation:Optimal anytime online learning on ℓp-balls. - Emmeran Johnson; David Martínez-Rubio; Ciara Pike-Burke; Patrick Rebeschini (CoRR, 2025)