My Publications

You can also find all my publications on Google Scholar.

Conference Publications

★ means equal contribution

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]

@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]

@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]

@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]

@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] [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]

@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]

@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

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

@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}
}

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

@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]

@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]

@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]

@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]

@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]

 
@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]

@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]

@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}
  }

David Martínez-Rubio

My Publications

Conference Publications

Preprints

Workshop Publications and Technical Reports

Theses