most citedLimitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization

5 citations · 5 across the 5 of their papers we have counts for

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5 papers

stat.ML2024

An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering +1

We study the performance of the Thompson Sampling algorithm for logistic bandit problems. In this setting, an agent receives binary rewards with probabilities determined by a logis…

cs.LG2024

A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry

Martin Lindström, Borja Rodríguez-Gálvez, Ragnar Thobaben +1

Hyperspherical Prototypical Learning (HPL) is a supervised approach to representation learning that designs class prototypes on the unit hypersphere. The prototypes bias the repres…

stat.ML2024

A note on generalization bounds for losses with finite moments

Borja Rodríguez-Gálvez, Omar Rivasplata, Ragnar Thobaben +1

This paper studies the truncation method from Alquier [1] to derive high-probability PAC-Bayes bounds for unbounded losses with heavy tails. Assuming that the -th moment is boun…

cs.LG2022★ 5 cited

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

Mahdi Haghifam, Borja Rodríguez-Gálvez, Ragnar Thobaben +3

To date, no "information-theoretic" frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic…

cs.LG2022

An Information-Theoretic Analysis of Bayesian Reinforcement Learning

Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering +1

Building on the framework introduced by Xu and Raginksy [1] for supervised learning problems, we study the best achievable performance for model-based Bayesian reinforcement learni…