L. Oneto

Luca Oneto, Alice Consilvio, Nicola Sacco, 2019, 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

Distribution-Dependent Weighted Union Bound †

S. Ridella, L. Oneto, 2021, Entropy.

Low-Resource Footprint, Data-Driven Malware Detection on Android

Luca Oneto, Francesco Palmieri, Alessio Merlo, 2020, IEEE Transactions on Sustainable Computing.

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2018, Neural Processing Letters.

Constraint-Aware Data Analysis on Mobile Devices: An Application to Human Activity Recognition on Smartphones

Davide Anguita, Luca Oneto, J. L. Ortiz, 2017 .

Quantum computing and supervised machine learning

Davide Anguita, Luca Oneto, Sandro Ridella, 2017 .

Semi-supervised Learning for Affective Common-Sense Reasoning

Davide Anguita, Luca Oneto, Erik Cambria, 2017, Cognitive Computation.

Learning Hardware-Friendly Classifiers Through Algorithmic Stability

Davide Anguita, Luca Oneto, Sandro Ridella, 2016, ACM Trans. Embed. Comput. Syst..

Performance assessment and uncertainty quantification of predictive models for smart manufacturing systems

Davide Anguita, Luca Oneto, Ilenia Orlandi, 2015, 2015 IEEE International Conference on Big Data (Big Data).

Fair regression via plug-in estimator and recalibration with statistical guarantees

Luca Oneto, Massimiliano Pontil, Evgenii Chzhen, 2020, NeurIPS.

Preliminaries

L. Oneto, 2019, Model Selection and Error Estimation in a Nutshell.

Complexity-Based Methods

Luca Oneto, L. Oneto, 2019, Model Selection and Error Estimation in a Nutshell.

Model selection and error estimation without the agonizing pain

Luca Oneto, L. Oneto, 2018, WIREs Data Mining Knowl. Discov..

A Deep Connection Between the Vapnik–Chervonenkis Entropy and the Rademacher Complexity

Davide Anguita, Luca Oneto, Sandro Ridella, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Some results about the Vapnik-Chervonenkis entropy and the rademacher complexity

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

General Fair Empirical Risk Minimization

Luca Oneto, Massimiliano Pontil, Michele Donini, 2019, 2020 International Joint Conference on Neural Networks (IJCNN).

Fairness in Machine Learning pdf

Luca Oneto, Silvia Chiappa, S. Chiappa, 2020, INNSBDDL.

Taking Advantage of Multitask Learning for Fair Classification

Luca Oneto, Massimiliano Pontil, Michele Donini, 2018, AIES.

Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory

Davide Anguita, Luca Oneto, Sandro Ridella, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Byte The Bullet: Learning on Real-World Computing Architectures

Luca Oneto, Alessandro Ghio, L. Oneto, 2014, ESANN.

Simple continuous optimal regions of the space of data

Luca Oneto, Francesca Cipollini, Alessio Carrega, 2019, Neurocomputing.

Emerging trends in machine learning: beyond conventional methods and data

Davide Anguita, Luca Oneto, Nicolò Navarin, 2018, ESANN.

A local Vapnik-Chervonenkis complexity

Davide Anguita, Luca Oneto, Sandro Ridella, 2016, Neural Networks.

Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf

Davide Anguita, Luca Oneto, Jorge Luis Reyes-Ortiz, 2015, INNS Conference on Big Data.

Algorithmic Stability Theory

Luca Oneto, L. Oneto, 2019, Model Selection and Error Estimation in a Nutshell.

PAC-bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis

Davide Anguita, Luca Oneto, Sandro Ridella, 2016, Pattern Recognit. Lett..

Can machine learning explain human learning?

Davide Anguita, Luca Oneto, Matthias Rauterberg, 2016, Neurocomputing.

Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, Neurocomputing.

Fully Empirical and Data-Dependent Stability-Based Bounds

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, IEEE Transactions on Cybernetics.

Fast convergence of extended Rademacher Complexity bounds

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, Neural Processing Letters.

Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, Neural Networks.

Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers

Davide Anguita, Luca Oneto, Sandro Ridella, 2014, Pattern Recognit. Lett..

An improved analysis of the Rademacher data-dependent bound using its self bounding property

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, Neural Networks.

In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, IEEE Transactions on Neural Networks and Learning Systems.

The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers

Davide Anguita, Luca Oneto, Sandro Ridella, 2011, NIPS.

Eleven quick tips for data cleaning and feature engineering

D. Chicco, L. Oneto, E. Tavazzi, 2022, PLoS Comput. Biol..

An Enhanced Random Forests Approach to Predict Heart Failure From Small Imbalanced Gene Expression Data

Luca Oneto, Davide Chicco, D. Chicco, 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification

Luca Oneto, Massimiliano Pontil, Evgenii Chzhen, 2019, NeurIPS.

Data-Driven Photovoltaic Power Production Nowcasting and Forecasting for Polygeneration Microgrids

Federico Delfino, Michela Robba, Davide Anguita, 2018, IEEE Systems Journal.

Advanced Analytics for Train Delay Prediction Systems by Including Exogenous Weather Data

Davide Anguita, Luca Oneto, Renzo Canepa, 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Machine learning approaches for improving condition-based maintenance of naval propulsion plants

Davide Anguita, Luca Oneto, Massimo Figari, 2016 .

Ship efficiency forecast based on sensors data collection: Improving numerical models through data analytics

Davide Anguita, Luca Oneto, Francesco Baldi, 2015, OCEANS 2015 - Genova.

The 'K' in K-fold Cross Validation

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, ESANN.

Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine

Davide Anguita, Luca Oneto, Alessandro Ghio, 2012, IWAAL.

In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, Neural Processing Letters.

Tikhonov, Ivanov and Morozov regularization for support vector machine learning

S. Ridella, L. Oneto, D. Anguita, 2016, Machine Learning.

Optimizing Fuel Consumption in Thrust Allocation for Marine Dynamic Positioning Systems

Davide Anguita, Luca Oneto, Andrea Coraddu, 2021, IEEE Transactions on Automation Science and Engineering.

Technical analysis and sentiment embeddings for market trend prediction

Luca Oneto, Erik Cambria, Yukun Ma, 2019, Expert Syst. Appl..

Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback

Davide Anguita, Luca Oneto, Andrea Coraddu, 2018, Reliab. Eng. Syst. Saf..

Computational Prediction of Propeller Cavitation Noise

L. Oneto, S. Gaggero, G. Tani, 2022, The 9th Conference on Computational Methods in Marine Engineering (Marine 2021).

Innovation Capability of Firms: A Big Data Approach with Patents

Luca Oneto, Raffaella Manzini, Linda Ponta, 2019, INNSBDDL.

Do we really need a new theory to understand over-parameterization?

S. Ridella, L. Oneto, D. Anguita, 2023, Neurocomputing.

Vessels fuel consumption forecast and trim optimisation: A data analytics perspective

Davide Anguita, Luca Oneto, Francesco Baldi, 2017 .

Transition-Aware Human Activity Recognition Using Smartphones

Davide Anguita, Luca Oneto, Albert Samà, 2016, Neurocomputing.

Differential privacy and generalization: Sharper bounds with applications

Davide Anguita, Luca Oneto, Sandro Ridella, 2017, Pattern Recognit. Lett..

Training Computationally Efficient Smartphone-Based Human Activity Recognition Models

Davide Anguita, Luca Oneto, Alessandro Ghio, 2013, ICANN.

Understanding Violin Players’ Skill Level Based on Motion Capture: a Data-Driven Perspective

Davide Anguita, Luca Oneto, Antonio Camurri, 2020, Cognitive Computation.

Mining Big Data with Random Forests

L. Oneto, D. Anguita, Alessandro Lulli, 2019, Cognitive Computation.

SLT-Based ELM for Big Social Data Analysis

Davide Anguita, Luca Oneto, Erik Cambria, 2017, Cognitive Computation.

Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma

Luca Oneto, Davide Chicco, D. Chicco, 2021, Health Informatics J..

A novelty detection approach to diagnosing hull and propeller fouling

Luca Oneto, Alan J. Murphy, Kayvan Pazouki, 2019, Ocean Engineering.

Digital twins of the mooring line tension for floating offshore wind turbines to improve monitoring, lifespan, and safety

L. Oneto, M. Collu, Andrea Coraddu, 2021, Journal of Ocean Engineering and Marine Energy.

Investigating Timing and Impact of News on the Stock Market

Luca Oneto, Erik Cambria, Yukun Ma, 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

Determining the most influential human factors in maritime accidents: A data-driven approach

Luca Oneto, Andrea Coraddu, Beatriz Navas de Maya, 2020 .

Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents

Davide Anguita, Luca Oneto, Andrea Coraddu, 2018, INNS Conference on Big Data.

Fair Regression with Wasserstein Barycenters pdf

Luca Oneto, Massimiliano Pontil, Evgenii Chzhen, 2020, NeurIPS.

Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis

Davide Anguita, Luca Oneto, Emanuele Fumeo, 2015, INNS Conference on Big Data.

Ensemble Application of Transfer Learning and Sample Weighting for Stock Market Prediction

Luca Oneto, Erik Cambria, Andrea Picasso Ratto, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Educational Simulator

Davide Anguita, Luca Oneto, Matthias Rauterberg, 2015, EC-TEL.

Sentic Computing for Social Network Analysis

E. Cambria, L. Oneto, F. Bisio, 2017 .

Statistical Learning Theory and ELM for Big Social Data Analysis

Davide Anguita, Luca Oneto, Erik Cambria, 2016, IEEE Computational Intelligence Magazine.

Dynamic Delay Predictions for Large-Scale Railway Networks: Deep and Shallow Extreme Learning Machines Tuned via Thresholdout

Davide Anguita, Luca Oneto, Renzo Canepa, 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Advances in learning analytics and educational data mining

Davide Anguita, Luca Oneto, Matthias Rauterberg, 2015, ESANN.

Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic

Davide Anguita, Luca Oneto, Alessandro Ghio, 2013, J. Univers. Comput. Sci..

Natural language processing for aviation safety: extracting knowledge from publicly-available loss of separation reports [version 2; peer review: 2 approved with reservations]

L. Oneto, A. Smoker, C. Dambra, 2022 .

Ensemble of Technical Analysis and Machine Learning for Market Trend Prediction

Luca Oneto, Erik Cambria, Yukun Ma, 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

Natural language processing for aviation safety: extracting knowledge from publicly-available loss of separation reports

Luca Oneto, Carlo Dambra, Anthony Smoker, 2021, Open Research Europe.

Data analytics and clinical feature ranking of medical records of patients with sepsis

Luca Oneto, Davide Chicco, D. Chicco, 2021, BioData Mining.

Unsupervised Deep Learning for Induction Motor Bearings Monitoring

Luca Oneto, Andrea Coraddu, Francesca Cipollini, 2019, Data-Enabled Discovery and Applications.

Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions

Davide Anguita, Luca Oneto, Alessandro Ghio, 2014, ICANN.

Human activity recognition on smartphones for mobile context awareness

Luca Oneto, Alessandro Ghio, J. L. Ortiz, 2012, NIPS 2012.

Learn and Visually Explain Deep Fair Models: an Application to Face Recognition

Davide Anguita, Luca Oneto, Nicolò Navarin, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

On the problem of recommendation for sensitive users and influential items: Simultaneously maintaining interest and diversity

Lamberto Ballan, L. Oneto, M. Monaro, 2023, Knowl. Based Syst..

A review on ship motions and quiescent periods prediction models

L. Oneto, Andrea Coraddu, Davide Anguita, 2023, Ocean Engineering.

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning

Luca Oneto, Massimiliano Pontil, Carlo Ciliberto, 2020, NeurIPS.

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Measuring the expressivity of graph kernels through Statistical Learning Theory

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2017, Neurocomputing.

Fairness in Machine Learning

S. Chiappa, L. Oneto, 2020, INNSBDDL.

A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks

L. Oneto, D. Anguita, Renzo Canepa, 2019, International Journal of Data Science and Analytics.

Computationally aware estimation of ultimate strength reduction of stiffened panels caused by welding residual stress: From finite element to data-driven methods

L. Oneto, Andrea Coraddu, Shen Li, 2022, Engineering Structures.

Maximal Discrepancy vs. Rademacher Complexity for error estimation

Davide Anguita, Luca Oneto, Sandro Ridella, 2011, ESANN.

Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates

S. Ridella, L. Oneto, D. Anguita, 2016, Neural Processing Letters.

Out-of-Sample Error Estimation: The Blessing of High Dimensionality

Davide Anguita, Luca Oneto, Sandro Ridella, 2014, 2014 IEEE International Conference on Data Mining Workshop.

Learning Fair and Transferable Representations with Theoretical Guarantees

Luca Oneto, Massimiliano Pontil, Michele Donini, 2020, 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA).

The benefits of adversarial defense in generalization

S. Ridella, L. Oneto, D. Anguita, 2022, Neurocomputing.

Local Rademacher Complexity Machine

Davide Anguita, Luca Oneto, Sandro Ridella, 2019, ESANN.

Randomized learning: Generalization performance of old and new theoretically grounded algorithms

Davide Anguita, Luca Oneto, Sandro Ridella, 2018, Neurocomputing.

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity

Alessandro Sperduti, Davide Anguita, Luca Oneto, 2016, ESANN.

Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints

Davide Anguita, Luca Oneto, Fabio Aiolli, 2016, ESANN.

A Learning Machine with a Bit-Based Hypothesis Space

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, ESANN.

In-Station Train Dispatching: A PDDL+ Planning Approach

Luca Oneto, Marco Maratea, Mauro Vallati, 2021, ICAPS.

A support vector machine classifier from a bit-constrained, sparse and localized hypothesis space

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Rademacher Complexity and Structural Risk Minimization: An Application to Human Gene Expression Datasets

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, ICANN.

Selecting the hypothesis space for improving the generalization ability of Support Vector Machines

Davide Anguita, Luca Oneto, Sandro Ridella, 2011, The 2011 International Joint Conference on Neural Networks.

Train Delay Prediction Systems: A Big Data Analytics Perspective

Davide Anguita, Luca Oneto, Renzo Canepa, 2017, Big Data Res..

Predicting Future Market Trends: Which Is the Optimal Window?

Luca Oneto, Erik Cambria, Andrea Picasso Ratto, 2019, INNSBDDL.

Deep Learning for the Generation of Heuristics in Answer Set Programming: A Case Study of Graph Coloring

L. Oneto, F. Ricca, Carmine Dodaro, 2022, LPNMR.

Shrinkage learning to improve SVM with hints

Davide Anguita, Luca Oneto, Sandro Ridella, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

In-sample model selection for Support Vector Machines

Davide Anguita, Luca Oneto, Sandro Ridella, 2011, The 2011 International Joint Conference on Neural Networks.

Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy

Massimiliano Pontil, Luca Oneto, John Shawe-Taylor, 2020, Neurocomputing.

Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine

G. Theotokatos, L. Oneto, R. Geertsma, 2021, Energy, Environment, and Sustainability.

Machine learning for wear forecasting of naval assets for condition-based maintenance applications

Davide Anguita, Luca Oneto, Alessandro Ghio, 2015, 2015 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles (ESARS).

Vessels Fuel Consumption: A Data Analytics Perspective to Sustainability

Davide Anguita, Luca Oneto, Francesco Baldi, 2018 .

Structural Risk Minimization and Rademacher Complexity for Regression

Davide Anguita, Luca Oneto, Sandro Ridella, 2012, ESANN.

Multilayer Graph Node Kernels: Stacking While Maintaining Convexity

A. Sperduti, L. Oneto, D. Anguita, 2018, Neural Processing Letters.

A Novel Procedure for Training L1-L2 Support Vector Machine Classifiers

Davide Anguita, Luca Oneto, Sandro Ridella, 2013, ICANN.

Learning Deep Fair Graph Neural Networks

Luca Oneto, Nicolò Navarin, Michele Donini, 2020, ESANN.

Do We Really Need a New Theory to Understand the Double-Descent?

S. Ridella, L. Oneto, D. Anguita, 2022, ESANN.