My Publications

You can also find all my publications on Google Scholar.

Conference Publications

★ means equal contribution

• Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point
  D. Martínez-Rubio★, C. Roux★, S. Pokutta
  ICML 2024, 41th International Conference on Machine Learning
  [PDF] [BibTeX]📋 copy

  @article{martinezrubio2023convergence,
    title={Convergence and Trade-Offs in Riemannian Gradient Descent and Riemannian Proximal Point},
    author={Mart{\'\i}nez-Rubio, David and Roux, Christophe and Pokutta, Sebastian},
    journal={arXiv preprint arXiv:2403.10429},
    url={https://arxiv.org/abs/2403.10429},
    year={2024}
  }
  

• Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond
  D. Martínez-Rubio★, E. Wirth★, and S. Pokutta
  COLT 2023, 36th Annual Conference on Learning Theory
  [PDF] [Slides] [BibTeX]📋 copy

  @article{martinezrubio2023pagerank,
    title={Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond},
    author={Mart{\'i}nez-Rubio, David and Wirth, Elias and Pokutta, Sebastian},
    booktitle    = {\textbf{COLT 2023}, Conference on Learning Theory 12-15 July 2023, {Bangalore}, {India}},
    year={2023}
  }
  

• Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties
  D. Martínez-Rubio, and S. Pokutta
  COLT 2023, 36th Annual Conference on Learning Theory
  [PDF] [Slides] [BibTeX]📋 copy

  @article{martinezRubio2022accelerated,
    title={Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties},
    author={Mart{\'i}nez-Rubio, David and Pokutta, Sebastian},
    booktitle    = {\textbf{COLT 2023}, Conference on Learning Theory 12-15 July 2023, {Bangalore}, {India}},
    journal={arXiv preprint arXiv:2211.14645},
    year={2022}
  }
  

• Fast Algorithms for Packing Proportional Fairness and its Dual
  F. Criado★, D. Martínez-Rubio★, S. Pokutta
  NeurIPS 2022, 35th Advances in Neural Information Processing Systems
  [PDF] [Slides] [BibTeX]📋 copy

  @article{criado2022fast,
    title={Fast algorithms for packing proportional fairness and its dual},
    author={Criado, Francisco and Mart{\'i}nez-Rubio, David and Pokutta, Sebastian},
    journal={Advances in Neural Information Processing Systems},
    volume={35},
    pages={25754--25766},
    url={https://arxiv.org/abs/2109.03678},
    year={2022}
  }
  

• Global Riemannian Acceleration in Hyperbolic and Spherical Spaces
  D. Martínez-Rubio
  ALT 2022, 33rd International Conference on Algorithmic Learning Theory
  [PDF] [Slides] [BibTeX]📋 copy

  @inproceedings{martinezRubio2020global,
    author    = {David Mart{\'{i}}nez{-}Rubio},
    editor    = {Sanjoy Dasgupta and
                 Nika Haghtalab},
    title     = {Global Riemannian Acceleration in Hyperbolic and Spherical Spaces},
    booktitle = {International Conference on Algorithmic Learning Theory, 29-1 April
                 2022, Paris, France},
    series    = {Proceedings of Machine Learning Research},
    volume    = {167},
    pages     = {768--826},
    publisher = ,
    year      = {2022},
    url       = {https://proceedings.mlr.press/v167/martinez-rubio22a.html},
  }
  
  
  

• Decentralized Cooperative Stochastic Bandits
  D. Martínez-Rubio, V. Kanade, and P. Rebeschini
  NeurIPS 2019, 32nd Advances in Neural Information Processing Systems
  [PDF] [Slides] [BibTeX]📋 copy [Code]

  @inproceedings{martinezRubio2018decentralized,
    author    = {David Mart{\'{i}}nez{-}Rubio and
                 Varun Kanade and
                 Patrick Rebeschini},
    editor    = {Hanna M. Wallach and
                 Hugo Larochelle and
                 Alina Beygelzimer and
                 Florence d'Alch{\'{e}}{-}Buc and
                 Emily B. Fox and
                 Roman Garnett},
    title     = {Decentralized Cooperative Stochastic Bandits},
    booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
                 on Neural Information Processing Systems 2019, NeurIPS 2019, December
                 8-14, 2019, Vancouver, BC, Canada},
    pages     = {4531--4542},
    year      = {2019},
    url       = {https://proceedings.neurips.cc/paper/2019/hash/85353d3b2f39b9c9b5ee3576578c04b7-Abstract.html},
  }
  

• Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
  M. Lezcano-Casado★, and D. Martínez-Rubio★
  ICML 2019, 36th International Conference on Machine Learning
  [PDF] [Slides] [BibTeX]📋 copy

  @inproceedings{DBLP:conf/icml/CasadoM19,
    author    = {Mario Lezcano Casado and
                 David Mart{\'{i}}nez{-}Rubio},
    editor    = {Kamalika Chaudhuri and
                 Ruslan Salakhutdinov},
    title     = {Cheap Orthogonal Constraints in Neural Networks: {A} Simple Parametrization
                 of the Orthogonal and Unitary Group},
    booktitle = {Proceedings of the 36th International Conference on Machine Learning,
                 {ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
    series    = {Proceedings of Machine Learning Research},
    volume    = {97},
    pages     = {3794--3803},
    publisher = ,
    year      = {2019},
    url       = {http://proceedings.mlr.press/v97/lezcano-casado19a.html},
  }
  
  

• Online Learning Rate Adaptation with Hypergradient Descent
  A. G. Baydin, R. Cornish, D. Martínez-Rubio, M. Schmidt, and F. Wood
  ICLR 2018, 6th International Conference on Learning Representations
  [PDF] [BibTeX]📋 copy

  @inproceedings{DBLP:conf/iclr/BaydinCMSW18,
    author    = {Atilim Gunes Baydin and
                 Robert Cornish and
                 David Mart{\'{i}}nez{-}Rubio and
                 Mark Schmidt and
                 Frank Wood},
    title     = {Online Learning Rate Adaptation with Hypergradient Descent},
    booktitle = {6th International Conference on Learning Representations, {ICLR} 2018,
                 Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings},
    publisher = {OpenReview.net},
    year      = {2018},
    url       = {https://openreview.net/forum?id=BkrsAzWAb},
  }
  

Preprints

• Non-Euclidean High-Order Smooth Convex Optimization
  J.P. Contreras★, C. Guzmán★, D. Martínez-Rubio★ (2024)
  [PDF] [BibTeX]📋 copy

  @article{contreras2024noneuclidean,
    title={Non-Euclidean High-Order Smooth Convex Optimization},
    author={Contreras, Juan Pablo and Guzmán, Cristóbal and Mart{\'\i}nez-Rubio, David},
    journal={arXiv preprint arXiv:2411.08987},
    url={https://arxiv.org/abs/2411.08987},
    year={2024}
  }
  

• Strong Convexity of Sets in Riemannian Manifolds
  D. Scieur★, T. Kerdreux★, D. Martínez-Rubio★, A. d'Aspremont, S. Pokutta (2023)
  [PDF] [BibTeX]📋 copy

  @article{scieur2023strong,
    title={Strong Convexity of Sets in Riemannian Manifolds},
    author={Scieur, Damien and Kerdreux, Thomas  and Mart{\'\i}nez-Rubio, David and d'Aspremont, Alexandre and Pokutta, Sebastian},
    journal={arXiv preprint arXiv:2312.03583},
    url={https://arxiv.org/abs/2312.03583},
    year={2023}
  }
  

• Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties
  D. Martínez-Rubio★, C. Roux★, C. Criscitiello, S. Pokutta (2023)
  [PDF] [BibTeX]📋 copy

  @article{martinez2023accelerated,
    title={Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties},
    author={Mart{\'\i}nez-Rubio, David and Roux, Christophe and Criscitiello, Christopher and Pokutta, Sebastian},
    journal={arXiv preprint arXiv:2305.16186},
    url={https://arxiv.org/abs/2305.16186},
    year={2023}
  }
  

• Neural Networks are a priori Biased Towards Boolean Functions with Low Entropy
  C. Mingard, J. Skalse, G. Valle-Pérez, D. Martínez-Rubio, V. Mikulik, and A. A. Louis (2019)
  [PDF] [BibTeX]📋 copy

  @article{mingard2019neural,
    title={Neural networks are a priori biased towards Boolean functions with low entropy},
    author={Mingard, Chris and Skalse, Joar and Valle-P{\'e}rez, Guillermo and Mart{\'i}nez-Rubio, David and Mikulik, Vladimir and Louis, Ard A},
    journal={arXiv preprint arXiv:1909.11522},
    url={http://arxiv.org/pdf/1909.11522.pdf},
    year={2019}
  }
  

Workshop Publications and Technical Reports

• Open Problem: Polynomial linearly-convergent method for g-convex optimization?
  C. Criscitiello★, D. Martínez-Rubio★, and N. Boumal★
  COLT 2023, 36th Annual Conference on Learning Theory
  [PDF] [BibTeX]📋 copy

  @inproceedings{criscitiello2023polynomial,
    author       = {Christopher Criscitiello and
                    David Mart{\'{\i}}nez{-}Rubio and
                    Nicolas Boumal},
    editor       = {Gergely Neu and
                    Lorenzo Rosasco},
    title        = {Open Problem: Polynomial linearly-convergent method for g-convex optimization?},
    booktitle    = {The Thirty Sixth Annual Conference on Learning Theory, {COLT} 2023,
                    12-15 July 2023, Bangalore, India},
    series       = {Proceedings of Machine Learning Research},
    volume       = {195},
    pages        = {5950--5956},
    publisher    = ,
    year         = {2023},
    url          = {https://proceedings.mlr.press/v195/criscitiello23b.html},
  }
  

• Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
  M. Lezcano-Casado, A.G. Baydin, D. Martínez-Rubio, T.A. Le, F. Wood, L. Heinrich, G. Louppe, K. Cranmer, K. Ng, W. Bhimji and Prabhat
  The Workshop on Deep Learning for Physical Sciences, NIPS 2017
  [PDF] [BibTeX]📋 copy

  @article{DBLP:journals/corr/abs-1712-07901,
    author    = {Mario Lezcano Casado and
                 Atilim Gunes Baydin and
                 David Mart{\'{i}}nez{-}Rubio and
                 Tuan Anh Le and
                 Frank D. Wood and
                 Lukas Heinrich and
                 Gilles Louppe and
                 Kyle Cranmer and
                 Karen Ng and
                 Wahid Bhimji and
                 Prabhat},
    title     = {Improvements to Inference Compilation for Probabilistic Programming
                 in Large-Scale Scientific Simulators},
    journal   = {CoRR},
    volume    = {abs/1712.07901},
    year      = {2017},
    url       = {http://arxiv.org/pdf/1712.07901.pdf},
    archivePrefix = {arXiv},
    eprint    = {1712.07901},
  }
  
  

Theses

• PhD Thesis
  Acceleration in First-Order Optimization Methods: Promenading Beyond Convexity or Smoothness, and Applications
  University of Oxford, 2021
  [PDF] [BibTeX]📋 copy

   
  @article{martinezrubio2021acceleration,
    title={Acceleration in First-Order Optimization Methods: Promenading Beyond Convexity or Smoothness, and Applications},
    author={Mart{\'i}nez-Rubio, David},
    journal={University of Oxford, Oxford, PhD thesis},
    url={https://damaru2.github.io/phd_thesis/thesis.pdf},
    year={2021}
  }

• MSc Thesis
  Convergence Analysis of an Adaptive Method of Gradient Descent
  University of Oxford, 2017
  [PDF] [BibTeX]📋 copy

  @article{martinezrubio2017convergence,
    title={Convergence analysis of an adaptive method of gradient descent},
    author={Mart{\'i}nez-Rubio, David},
    journal={University of Oxford, Oxford, M. Sc. thesis},
    url={https://damaru2.github.io/convergence_analysis_hypergradient_descent/dissertation_hypergradients.pdf},
    year={2017}
  }

• BSc Thesis
  Characterization and computation of the Galois group of polynomials of degree 7
  Complutense University of Madrid, 2016
  [PDF] [BibTeX]📋 copy

  @article{martinezrubio2016characterization,
    title={Characterization and computation of the Galois group of polynomials of degree 7},
    author={Mart{\'i}nez-Rubio, David},
    journal={Complutense University of Madrid, Madrid, B.Sc. Thesis},
    url={https://damaru2.github.io/assets/TFG.pdf},
    year={2016}
    }