David Martínez-Rubio

Preprints

1. Martínez-Rubio, D., Roux, C., and Pokutta, S. (2024). Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point. [arXiv]
   [BibTeX]

   @misc{mrp_tradeoffs_riemannian_gradient_descent_23,
     archiveprefix = {arXiv},
     eprint = {2403.10429},
     primaryclass = {math.OC},
     year = {2024},
     author = {Martínez-Rubio, David and Roux, Christophe and Pokutta, Sebastian},
     title = {Convergence and Trade-offs in Riemannian Gradient Descent and Riemannian Proximal Point}
   }

2. Scieur, D., Kerdreux, T., Martínez-Rubio, D., d’Aspremont, A., and Pokutta, S. (2023). Strong Convexity of Sets in Riemannian Manifolds. [arXiv]
   [BibTeX]

   @misc{skmap_strong_convexity_of_sets_in_riemannian_manifolds_23,
     archiveprefix = {arXiv},
     eprint = {2312.03583},
     primaryclass = {math.OC},
     year = {2023},
     author = {Scieur, Damien and Kerdreux, Thomas and Martínez-Rubio, David and d'Aspremont, Alexandre and Pokutta, Sebastian},
     title = {Strong Convexity of Sets in Riemannian Manifolds}
   }

Conference proceedings

1. Criscitiello, C., Martínez-Rubio, D., and Boumal, N. (2023). Open Problem: Polynomial Linearly-convergent Method for G-convex Optimization? Proceedings of Annual Workshop on Computational Learning Theory. [arXiv]
   [BibTeX]

   @inproceedings{cmb_open_problem_polynomial_linearly_convergent_method_for_gconvex_optimization_23,
     year = {2023},
     booktitle = {Proceedings of Annual Workshop on Computational Learning Theory},
     archiveprefix = {arXiv},
     eprint = {2307.12743},
     primaryclass = {math.OC},
     author = {Criscitiello, Christopher and Martínez-Rubio, David and Boumal, Nicolas},
     title = {Open Problem: Polynomial Linearly-convergent Method for G-convex Optimization?}
   }

2. Martínez-Rubio, D., and Pokutta, S. (2023). Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. Proceedings of Annual Workshop on Computational Learning Theory. [arXiv]
   [BibTeX]

   @inproceedings{mp_acceleratedriemannian_22:1,
     year = {2023},
     booktitle = {Proceedings of Annual Workshop on Computational Learning Theory},
     archiveprefix = {arXiv},
     eprint = {2211.14645},
     primaryclass = {math.OC},
     author = {Martínez-Rubio, David and Pokutta, Sebastian},
     title = {Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties}
   }

3. Martínez-Rubio, D., Roux, C., Criscitiello, C., and Pokutta, S. (2023). Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties. Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2023). [arXiv]
   [BibTeX]

   @inproceedings{mrp_accelerated_minmax_riemannian_23,
     year = {2023},
     booktitle = {Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2023)},
     archiveprefix = {arXiv},
     eprint = {2305.16186},
     primaryclass = {math.OC},
     author = {Martínez-Rubio, David and Roux, Christophe and Criscitiello, Christopher and Pokutta, Sebastian},
     title = {Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties}
   }

4. Martínez-Rubio, D., Wirth, E., and Pokutta, S. (2023). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of Annual Workshop on Computational Learning Theory. [arXiv]
   [BibTeX]

   @inproceedings{mwp_accelerated_sparse_pagerank_23,
     year = {2023},
     booktitle = {Proceedings of Annual Workshop on Computational Learning Theory},
     archiveprefix = {arXiv},
     eprint = {2303.12875},
     primaryclass = {math.OC},
     author = {Martínez-Rubio, David and Wirth, Elias and Pokutta, Sebastian},
     title = {Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond}
   }

5. Criado, F., Martínez-Rubio, D., and Pokutta, S. (2022). Fast Algorithms for Packing Proportional Fairness and Its Dual. Proceedings of Conference on Neural Information Processing Systems. [arXiv] [poster]
   [BibTeX]

   @inproceedings{cmp_packing_proportional_fairness_22,
     year = {2022},
     booktitle = {Proceedings of Conference on Neural Information Processing Systems},
     archiveprefix = {arXiv},
     eprint = {2109.03678},
     primaryclass = {math.OC},
     author = {Criado, Francisco and Martínez-Rubio, David and Pokutta, Sebastian},
     title = {Fast Algorithms for Packing Proportional Fairness and Its Dual},
     poster = {https://pokutta.com/slides/20211105_fairpacking-poster.pdf}
   }

6. Martínez-Rubio, D., and Pokutta, S. (2022). Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties. Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2022). [URL] [arXiv] [poster]
   [BibTeX]

   @inproceedings{mp_acceleratedriemannian_22,
     year = {2022},
     booktitle = {Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2022)},
     url = {https://opt-ml.org/papers/2022/paper27.pdf},
     archiveprefix = {arXiv},
     eprint = {2211.14645},
     primaryclass = {math.OC},
     author = {Martínez-Rubio, David and Pokutta, Sebastian},
     title = {Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties},
     poster = {https://pokutta.com/slides/20221203_poster_neurips_riemannian.pdf}
   }