David Martínez-Rubio

Orcid: 0000-0002-4345-5422

Affiliations:
  • Zuse Institute Berlin (ZIB), Laboratory for Interactive Optimization and Learning, Germany
  • TU Berlin, Germany
  • University of Oxford, Department of Computer Science, UK (former, PhD 2021)


According to our database1, David Martínez-Rubio authored at least 13 papers between 2017 and 2023.

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Bibliography

2023
Open Problem: Polynomial linearly-convergent method for geodesically convex optimization?
CoRR, 2023

Accelerated Methods for Riemannian Min-Max Optimization Ensuring Bounded Geometric Penalties.
CoRR, 2023

Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Open Problem: Polynomial linearly-convergent method for g-convex optimization?
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Fast Algorithms for Packing Proportional Fairness and its Dual.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Global Riemannian Acceleration in Hyperbolic and Spherical Spaces.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2019
Neural networks are a priori biased towards Boolean functions with low entropy.
CoRR, 2019

Decentralized Cooperative Stochastic Bandits.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Decentralized Cooperative Stochastic Multi-armed Bandits.
CoRR, 2018

Online Learning Rate Adaptation with Hypergradient Descent.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators.
CoRR, 2017


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