44 citations · 62 across the 7 of their papers we have counts for
7 papers
Generalization and Informativeness of Conformal Prediction
Matteo Zecchin, Sangwoo Park, Osvaldo Simeone +1
The safe integration of machine learning modules in decision-making processes hinges on their ability to quantify uncertainty. A popular technique to achieve this goal is conformal…
Comparing Comparators in Generalization Bounds
Fredrik Hellström, Benjamin Guedj
We derive generic information-theoretic and PAC-Bayesian generalization bounds involving an arbitrary convex comparator function, which measures the discrepancy between the trainin…
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj +1
A fundamental question in theoretical machine learning is generalization. Over the past decades, the PAC-Bayesian approach has been established as a flexible framework to address t…
Adaptive Selective Sampling for Online Prediction with Experts
Rui M. Castro, Fredrik Hellström, Tim van Erven
We consider online prediction of a binary sequence with expert advice. For this setting, we devise label-efficient forecasting algorithms, which use a selective sampling scheme tha…
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness
Fredrik Hellström, Giuseppe Durisi
Recent work has established that the conditional mutual information (CMI) framework of Steinke and Zakynthinou (2020) is expressive enough to capture generalization guarantees in t…
A New Family of Generalization Bounds Using Samplewise Evaluated CMI
Fredrik Hellström, Giuseppe Durisi
We present a new family of information-theoretic generalization bounds, in which the training loss and the population loss are compared through a jointly convex function. This func…
Generalization Bounds via Information Density and Conditional Information Density
Fredrik Hellström, Giuseppe Durisi
We present a general approach, based on exponential inequalities, to derive bounds on the generalization error of randomized learning algorithms. Using this approach, we provide bo…