Borja Rodríguez Gálvez

According to our database, Borja Rodríguez Gálvez authored at least 19 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

An Information-Theoretic Approach to Generalization Theory.

Borja Rodríguez Gálvez

PhD thesis, 2024

More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity.

Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund

J. Mach. Learn. Res., 2024

Information-Theoretic Minimax Regret Bounds for Reinforcement Learning based on Duality.

Raghav Bongole, Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias J. Oechtering, Mikael Skoglund

CoRR, 2024

An Information-Theoretic Approach to Generalization Theory.

Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund

CoRR, 2024

On Information Theoretic Fairness: Compressed Representations With Perfect Demographic Parity.

Amirreza Zamani, Borja Rodríguez Gálvez, Mikael Skoglund

CoRR, 2024

A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry.

Martin Lindström, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund

CoRR, 2024

Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems.

Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias J. Oechtering, Mikael Skoglund

CoRR, 2024

A Note on Generalization Bounds for Losses with Finite Moments.

Borja Rodríguez Gálvez, Omar Rivasplata, Ragnar Thobaben, Mikael Skoglund

Proceedings of the IEEE International Symposium on Information Theory, 2024

2023

Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards.

Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias J. Oechtering, Mikael Skoglund

Proceedings of the IEEE International Symposium on Information Theory, 2023

The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning.

Borja Rodríguez Gálvez, Arno Blaas, Pau Rodríguez, Adam Golinski, Xavier Suau, Jason Ramapuram, Dan Busbridge, Luca Zappella

Proceedings of the International Conference on Machine Learning, 2023

Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization.

Mahdi Haghifam, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

An Information-Theoretic Analysis of Bayesian Reinforcement Learning.

Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias J. Oechtering, Mikael Skoglund

Proceedings of the 58th Annual Allerton Conference on Communication, 2022

2021

Upper Bounds on the Generalization Error of Private Algorithms for Discrete Data.

Borja Rodríguez Gálvez, Germán Bassi, Mikael Skoglund

IEEE Trans. Inf. Theory, 2021

Enforcing fairness in private federated learning via the modified method of differential multipliers.

Borja Rodríguez Gálvez, Filip Granqvist, Rogier C. van Dalen, Matt Seigel

CoRR, 2021

Tighter Expected Generalization Error Bounds via Wasserstein Distance.

Borja Rodríguez Gálvez, Germán Bassi, Ragnar Thobaben, Mikael Skoglund

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Variational Approach to Privacy and Fairness.

Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund

Proceedings of the IEEE Information Theory Workshop, 2021

2020

The Convex Information Bottleneck Lagrangian.

Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund

Entropy, 2020

Upper Bounds on the Generalization Error of Private Algorithms.

Borja Rodríguez Gálvez, Germán Bassi, Mikael Skoglund

CoRR, 2020

On Random Subset Generalization Error Bounds and the Stochastic Gradient Langevin Dynamics Algorithm.

Borja Rodríguez Gálvez, Germán Bassi, Ragnar Thobaben, Mikael Skoglund

Proceedings of the IEEE Information Theory Workshop, 2020