| 2025 | AISTATS | Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. | David Martnez-Rubio, Christophe Roux, Christopher Criscitiello, Sebastian Pokutta |
| 2025 | AISTATS | Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization. | Simon Vary, David Martnez-Rubio, Patrick Rebeschini |
| 2025 | COLT | Non-Euclidean High-Order Smooth Convex Optimization Extended Abstract. | Juan Pablo Contreras, Cristbal Guzmn, David Martnez-Rubio |
| 2025 | ICML | Secant Line Search for Frank-Wolfe Algorithms. | Deborah Hendrych, Sebastian Pokutta, Mathieu Besanon, David Martnez-Rubio |
| 2025 | ICML | Implicit Riemannian Optimism with Applications to Min-Max Problems. | Christophe Roux, David Martnez-Rubio, Sebastian Pokutta |
| 2024 | ICML | Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point. | David Martnez-Rubio, Christophe Roux, Sebastian Pokutta |
| 2023 | COLT | Open Problem: Polynomial linearly-convergent method for g-convex optimization? | Christopher Criscitiello, David Martnez-Rubio, Nicolas Boumal |
| 2023 | COLT | Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. | David Martnez-Rubio, Sebastian Pokutta |
| 2023 | COLT | Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. | David Martnez-Rubio, Elias Samuel Wirth, Sebastian Pokutta |
| 2022 | ALT | Global Riemannian Acceleration in Hyperbolic and Spherical Spaces. | David Martnez-Rubio |
| 2019 | ICML | Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group. | Mario Lezcano Casado, David Martnez-Rubio |
| 2018 | ICLR | Online Learning Rate Adaptation with Hypergradient Descent. | Atilim Gunes Baydin, Robert Cornish, David Martnez-Rubio, Mark Schmidt, Frank Wood |