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20222024
most citedAn Information-Theoretic Approach to Generalization Theory

1 citations · 1 across the 6 of their papers we have counts for

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6 papers

cs.IT2024

On Information Theoretic Fairness: Compressed Representations With Perfect Demographic Parity

Amirreza Zamani, Borja Rodríguez-Gálvez, Mikael Skoglund

In this article, we study the fundamental limits in the design of fair and/or private representations achieving perfect demographic parity and/or perfect privacy through the lens o…

stat.ML20241 cited

An Information-Theoretic Approach to Generalization Theory

Borja Rodríguez-Gálvez, Ragnar Thobaben, Mikael Skoglund

We investigate the in-distribution generalization of machine learning algorithms. We depart from traditional complexity-based approaches by analyzing information-theoretic bounds t…

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…

stat.ML2024

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

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

This paper studies the Bayesian regret of a variant of the Thompson-Sampling algorithm for bandit problems. It builds upon the information-theoretic framework of [Russo and Van Roy…

stat.ML2023

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

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

In this work, we study the performance of the Thompson Sampling algorithm for Contextual Bandit problems based on the framework introduced by Neu et al. and their concept of lifted…

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…