L
Luca Oneto
Researcher at University of Genoa
Publications - 195
Citations - 7058
Luca Oneto is an academic researcher from University of Genoa. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 29, co-authored 169 publications receiving 5046 citations. Previous affiliations of Luca Oneto include University of Pisa.
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Proceedings Article
A public domain dataset for human activity recognition using smartphones
TL;DR: An Activity Recognition database is described, built from the recordings of 30 subjects doing Activities of Daily Living while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository.
Book ChapterDOI
Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine
TL;DR: This paper presents a system for human physical Activity Recognition using smartphone inertial sensors and proposes a novel hardware-friendly approach for multiclass classification that adapts the standard Support Vector Machine and exploits fixed-point arithmetic for computational cost reduction.
Journal ArticleDOI
Transition-Aware Human Activity Recognition Using Smartphones
TL;DR: Results show that TAHAR outperforms state-of-the-art baseline works and reveal the main advantages of the architecture.
Book ChapterDOI
Fairness in Machine Learning
Luca Oneto,Silvia Chiappa +1 more
TL;DR: It is shown how causal Bayesian networks can play an important role to reason about and deal with fairness, especially in complex unfairness scenarios, and how optimal transport theory can be leveraged to develop methods that impose constraints on the full shapes of distributions corresponding to different sensitive attributes.
Proceedings Article
Empirical Risk Minimization Under Fairness Constraints
TL;DR: This work presents an approach based on empirical risk minimization, which incorporates a fairness constraint into the learning problem, and derives both risk and fairness bounds that support the statistical consistency of the approach.