David Martínez-Rubio

postdoctoral researcher at ZIB since January 2022

📬 Contact

office Room 3107 at ZIB
Room MA 604 at TUB
e-mail martinez-rubio (at) zib.de
homepage damaru2.github.io
languages English, Spanish, and Toki Pona

🎓 Academic Background

Jan 2022 Ph.D. in Computer Science at Oxford
Aug 2017 M.Sc. in Mathematics and Foundations of Computer Science at Oxford
Jul 2016 B.Sc. in Computer Science and Engineering at UCM
Jul 2016 B.Sc. in Mathematics at UCM

🔬 Research

I am mainly interested in optimization and online learning with a focus on high dimensional problems. I have worked on non-linear convex and non-convex problems, Riemannian geodesically convex optimization, accelerated algorithms, PageRank, packing, and bandit problems.

Conference proceedings

  1. 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}
}
  2. 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,
  booktitle = {Proceedings of Optimization for Machine Learning (NeurIPS Workshop OPT 2023)},
  archiveprefix = {arXiv},
  eprint = {2305.16186},
  primaryclass = {math.OC},
  year = {2023},
  author = {Martínez-Rubio, David and Roux, Christophe and Criscitiello, Christopher and Pokutta, Sebastian},
  title = {Accelerated Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties}
}
  3. 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}
}
  4. 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}
}
  5. 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,
  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},
  year = {2022},
  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}
}