Borja Rodríguez Gálvez

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 23

Top Topics

Generalization Error

Bayesian Reinforcement Learning

Information Bottleneck

Convex Optimization

Top Venues

IEEE Trans. Inf. Theory

Publications

Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias J. Oechtering, Mikael Skoglund
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems. CoRR (2024)
Borja Rodríguez Gálvez, Omar Rivasplata, Ragnar Thobaben, Mikael Skoglund
A note on generalization bounds for losses with finite moments. CoRR (2024)
Martin Lindström, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund
A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry. CoRR (2024)
Borja Rodríguez Gálvez, Arno Blaas, Pau Rodríguez, Adam Golinski, Xavier Suau, Jason Ramapuram, Dan Busbridge, Luca Zappella
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning. ICML (2023)
Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund
More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime-validity. CoRR (2023)
Borja Rodríguez Gálvez, Arno Blaas, Pau Rodríguez, Adam Golinski, Xavier Suau, Jason Ramapuram, Dan Busbridge, Luca Zappella
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning. CoRR (2023)
Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias J. Oechtering, Mikael Skoglund
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards. ISIT (2023)
Mahdi Haghifam, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. ALT (2023)
Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias J. Oechtering, Mikael Skoglund
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards. CoRR (2023)
Mahdi Haghifam, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. CoRR (2022)
Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias J. Oechtering, Mikael Skoglund
An Information-Theoretic Analysis of Bayesian Reinforcement Learning. CoRR (2022)
Amaury Gouverneur, Borja Rodríguez Gálvez, Tobias J. Oechtering, Mikael Skoglund
An Information-Theoretic Analysis of Bayesian Reinforcement Learning. Allerton (2022)
Borja Rodríguez Gálvez, Germán Bassi, Ragnar Thobaben, Mikael Skoglund
Tighter Expected Generalization Error Bounds via Wasserstein Distance. NeurIPS (2021)
Borja Rodríguez Gálvez, Germán Bassi, Ragnar Thobaben, Mikael Skoglund
Tighter expected generalization error bounds via Wasserstein distance. CoRR (2021)
Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund
A Variational Approach to Privacy and Fairness. ITW (2021)
Borja Rodríguez Gálvez, Filip Granqvist, Rogier C. van Dalen, Matt Seigel
Enforcing fairness in private federated learning via the modified method of differential multipliers. CoRR (2021)
Borja Rodríguez Gálvez, Germán Bassi, Mikael Skoglund
Upper Bounds on the Generalization Error of Private Algorithms for Discrete Data. IEEE Trans. Inf. Theory 67 (11) (2021)
Borja Rodríguez Gálvez, Germán Bassi, Ragnar Thobaben, Mikael Skoglund
On Random Subset Generalization Error Bounds and the Stochastic Gradient Langevin Dynamics Algorithm. CoRR (2020)
Borja Rodríguez Gálvez, Germán Bassi, Mikael Skoglund
Upper Bounds on the Generalization Error of Private Algorithms. CoRR (2020)
Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund
The Convex Information Bottleneck Lagrangian. Entropy 22 (1) (2020)
Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund
A Variational Approach to Privacy and Fairness. CoRR (2020)
Borja Rodríguez Gálvez, Germán Bassi, Ragnar Thobaben, Mikael Skoglund
On Random Subset Generalization Error Bounds and the Stochastic Gradient Langevin Dynamics Algorithm. ITW (2020)
Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund
The Convex Information Bottleneck Lagrangian. CoRR (2019)