Caltech Hypercube MIMD Computer Performances Measurements in a Physical Mathematical Application. - Claudio Martini; Mauro Morando; Sandro Ridella (CONPAR, 1986)

LU Factorization with Maximum Performances on FPS Architectures 38/64 BIT. - Angelo Corana; Claudio Martini; Sandro Ridella; Claudia Rolando (ICS, 1987)

Minimizing multimodal functions of continuous variables with the "simulated annealing" algorithm. - Angelo Corana; Michele Marchesi; Claudio Martini; Sandro Ridella (ACM Trans. Math. Softw., 1987)

Solving linear equation systems on vector computers with maximum efficiency. - A. Corona; Claudio Martini; Mauro Morando; Sandro Ridella; Claudia Rolando (Parallel Comput., 1988)

Corrigenda: "Minimizing Multimodal Functions of Continuous Variables with the 'Simulated Annealing' Algorithm". - Angelo Corana; Claudio Martini; Sandro Ridella (ACM Trans. Math. Softw., 1989)

Efficient computation of the correlation dimension from a time series on a LIW computer. - Angelo Corana; Aldo Casaleggio; Claudia Rolando; Sandro Ridella (Parallel Comput., 1991)

Global Optimization of Functions with the Interval Genetic Algorithm. - Marco Muselli; Sandro Ridella (Complex Syst., 1992)

A dedicated massively parallel architecture for the Boltzmann machine. - Alessandro De Gloria; Paolo Faraboschi; Sandro Ridella (Parallel Comput., 1992)

Statistically controlled activation weight initialization (SCAWI). - Gian Paolo Drago; Sandro Ridella (IEEE Trans. Neural Networks, 1992)

Pruning and rule extraction using class entropy. - Sandro Ridella; Gian Luca Speroni; Paolo Trebino; Rodolfo Zunino (ICNN, 1993)

Using chaos to generate keys for associative noise-like coding memories. - Giancarlo Parodi; Sandro Ridella; Rodolfo Zunino (Neural Networks, 1993)

Convergence Properties of Cascade Correlation in Function Approximation. - Gian Paolo Drago; Sandro Ridella (Neural Comput. Appl., 1994)

Class-Entropy Minimisation Networks for Domain Analysis and Rule Extraction. - Sandro Ridella; Gian Luca Speroni; Paolo Trebino; Rodolfo Zunino (Neural Comput. Appl., 1994)

Learning the appropriate representation paradigm by circular processing units. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (ESANN, 1995)

An Adaptive Momentum Back Propagation (AMBP). - Gian Paolo Drago; Mauro Morando; Sandro Ridella (Neural Comput. Appl., 1995)

Adaptive Internal Representation in Circular Back-Propagation Networks. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (Neural Comput. Appl., 1995)

Limiting the effects of weight errors in feedforward networks using interval arithmetic. - Davide Anguita; Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (ICNN, 1996)

On the convergence of a growing topology neural algorithm. - Gian Paolo Drago; Sandro Ridella (Neurocomputing, 1996)

On the importance of sorting in "neural gas" training of vector quantizers. - Fabio Ancona; Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (ICNN, 1997)

CBP networks as a generalized neural model. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (ICNN, 1997)

Circular backpropagation networks for classification. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (IEEE Trans. Neural Networks, 1997)

Pruning with interval arithmetic perceptron. - Gian Paolo Drago; Sandro Ridella (Neurocomputing, 1998)

Plastic Algorithm for Adaptive Vector Quantisation. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (Neural Comput. Appl., 1998)

Support Vector Machines: A Comparison of Some Kernel Functions. - Davide Anguita; Andrea Boni; Sandro Ridella (IIA/SOCO, 1999)

A VLSI friendly algorithm for support vector machines. - Davide Anguita; Andrea Boni; Sandro Ridella (IJCNN, 1999)

Possibility and Necessity Pattern Classification using an Interval Arithmetic Perceptron. - Gian Paolo Drago; Sandro Ridella (Neural Comput. Appl., 1999)

Worst case analysis of weight inaccuracy effects in multilayer perceptrons. - Davide Anguita; Sandro Ridella; Stefano Rovetta (IEEE Trans. Neural Networks, 1999)

Representation and generalization properties of class-entropy networks. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (IEEE Trans. Neural Networks, 1999)

Circular backpropagation networks embed vector quantization. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (IEEE Trans. Neural Networks, 1999)

The K-Winner Machine Model. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (IJCNN (1), 2000)

Augmenting vector quantization with interval arithmetics for image-coding applications. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (ISCAS, 2000)

Digital VLSI Algorithms and Architectures for Support Vector Machines. - Davide Anguita; Andrea Boni; Sandro Ridella (Int. J. Neural Syst., 2000)

Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap. - Davide Anguita; Andrea Boni; Sandro Ridella (Neural Process. Lett., 2000)

K-winner machines for pattern classification. - Sandro Ridella; Stefano Rovetta; Rodolfo Zunino (IEEE Trans. Neural Networks, 2001)

Empirical measure of multiclass generalization performance: the K-winner machine case. - Sandro Ridella; Rodolfo Zunino (IEEE Trans. Neural Networks, 2001)

Automatic Hyperparameter Tuning for Support Vector Machines. - Davide Anguita; Sandro Ridella; Fabio Rivieccio; Rodolfo Zunino (ICANN, 2002)

SVM learning with fixed-point math. - Davide Anguita; Andrea Boni; Sandro Ridella (IJCNN, 2003)

Training support vector machines: a quantum-computing perspective. - Davide Anguita; Sandro Ridella; Fabio Rivieccio; Rodolfo Zunino (IJCNN, 2003)

A model-selection approach to the VLSI design of vector quantizers. - Massimiliano Bracco; Sandro Ridella; Rodolfo Zunino (IJCNN, 2003)

Hyperparameter design criteria for support vector classifiers. - Davide Anguita; Sandro Ridella; Fabio Rivieccio; Rodolfo Zunino (Neurocomputing, 2003)

Quantum optimization for training support vector machines. - Davide Anguita; Sandro Ridella; Fabio Rivieccio; Rodolfo Zunino (Neural Networks, 2003)

A digital architecture for support vector machines: theory, algorithm, and FPGA implementation. - Davide Anguita; Andrea Boni; Sandro Ridella (IEEE Trans. Neural Networks, 2003)

Digital implementation of hierarchical vector quantization. - Massimiliano Bracco; Sandro Ridella; Rodolfo Zunino (IEEE Trans. Neural Networks, 2003)

Unsupervised clustering and the capacity of support vector machines. - Davide Anguita; Sandro Ridella; Fabio Rivieccio; Rodolfo Zunino (IJCNN, 2004)

A new method for multiclass support vector machines. - Davide Anguita; Sandro Ridella; Dario Sterpi (IJCNN, 2004)

Model selection in top quark tagging with a support vector classifier. - Davide Artguita; Sandro Ridella; Silvia Amerio; Ignazio Lazzizzera (IJCNN, 2004)

Vector quantization complexity and quantum computing. - Paolo Gastaldo; Sandro Ridella; Rodolfo Zunino (IJCNN, 2004)

An Algorithm for Reducing the Number of Support Vectors. - Davide Anguita; Sandro Ridella; Fabio Rivieccio (WIRN, 2004)

Using K-Winner Machines for domain analysis. - Sandro Ridella; Rodolfo Zunino (Neurocomputing, 2004)

K-fold generalization capability assessment for support vector classifiers. - Davide Anguita; Sandro Ridella; Fabio Rivieccio (IJCNN, 2005)

Testing the Augmented Binary Multiclass SVM on Microarray Data. - Davide Anguita; Sandro Ridella; Dario Sterpi (IJCNN, 2006)

Prospects of quantum-classical optimization for digital design. - Paolo Gastaldo; Sandro Ridella; Rodolfo Zunino (Appl. Math. Comput., 2006)

Feed-Forward Support Vector Machine Without Multipliers. - Davide Anguita; Stefano Pischiutta; Sandro Ridella; Dario Sterpi (IEEE Trans. Neural Networks, 2006)

A Hardware-friendly Support Vector Machine for Embedded Automotive Applications. - Davide Anguita; Alessandro Ghio; Stefano Pischiutta; Sandro Ridella (IJCNN, 2007)

Using Variable Neighborhood Search to improve the Support Vector Machine performance in embedded automotive applications. - Enrique Alba 001; Davide Anguita; Alessandro Ghio; Sandro Ridella (IJCNN, 2008)

A support vector machine with integer parameters. - Davide Anguita; Alessandro Ghio; Stefano Pischiutta; Sandro Ridella (Neurocomputing, 2008)

K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines. - Davide Anguita; Alessandro Ghio; Sandro Ridella; Dario Sterpi (DMIN, 2009)

Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory. - Davide Anguita; Alessandro Ghio; Noemi Greco; Luca Oneto; Sandro Ridella (IJCNN, 2010)

A neural model approach for regularization in the mean estimation case. - Sergio Decherchi; Mauro Parodi; Sandro Ridella (IJCNN, 2010)

Using unsupervised analysis to constrain generalization bounds for support vector classifiers. - Sergio Decherchi; Sandro Ridella; Rodolfo Zunino; Paolo Gastaldo; Davide Anguita (IEEE Trans. Neural Networks, 2010)

Maximal Discrepancy vs. Rademacher Complexity for error estimation. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (ESANN, 2011)

Test error bounds for classifiers: A survey of old and new results. - Davide Anguita; Luca Ghelardoni; Alessandro Ghio; Sandro Ridella (FOCI, 2011)

In-sample model selection for Support Vector Machines. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (IJCNN, 2011)

Selecting the hypothesis space for improving the generalization ability of Support Vector Machines. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (IJCNN, 2011)

The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers. - Luca Oneto; Davide Anguita; Alessandro Ghio; Sandro Ridella (NIPS, 2011)

Maximal Discrepancy for Support Vector Machines. - Davide Anguita; Alessandro Ghio; Sandro Ridella (Neurocomputing, 2011)

A FPGA Core Generator for Embedded Classification Systems. - Davide Anguita; Luca Carlino; Alessandro Ghio; Sandro Ridella (J. Circuits Syst. Comput., 2011)

The 'K' in K-fold Cross Validation. - Davide Anguita; Luca Ghelardoni; Alessandro Ghio; Luca Oneto; Sandro Ridella (ESANN, 2012)

Structural Risk Minimization and Rademacher Complexity for Regression. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (ESANN, 2012)

Nested Sequential Minimal Optimization for Support Vector Machines. - Alessandro Ghio; Davide Anguita; Luca Oneto; Sandro Ridella; Carlotta Schatten (ICANN (2), 2012)

Rademacher Complexity and Structural Risk Minimization: An Application to Human Gene Expression Datasets. - Luca Oneto; Davide Anguita; Alessandro Ghio; Sandro Ridella (ICANN (2), 2012)

Learning the mean: A neural network approach. - Sergio Decherchi; Mauro Parodi; Sandro Ridella (Neurocomputing, 2012)

In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (Neural Process. Lett., 2012)

In-Sample and Out-of-Sample Model Selection and Error Estimation for Support Vector Machines. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (IEEE Trans. Neural Networks Learn. Syst., 2012)

A Learning Machine with a Bit-Based Hypothesis Space. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (ESANN, 2013)

A Novel Procedure for Training L1-L2 Support Vector Machine Classifiers. - Davide Anguita; Alessandro Ghio; Luca Oneto; Jorge Luis Reyes-Ortiz; Sandro Ridella (ICANN, 2013)

Some results about the Vapnik-Chervonenkis entropy and the rademacher complexity. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (IJCNN, 2013)

A support vector machine classifier from a bit-constrained, sparse and localized hypothesis space. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (IJCNN, 2013)

A Survey of old and New Results for the Test Error Estimation of a Classifier. - Davide Anguita; Luca Ghelardoni; Alessandro Ghio; Sandro Ridella (J. Artif. Intell. Soft Comput. Res., 2013)

An improved analysis of the Rademacher data-dependent bound using its self bounding property. - Luca Oneto; Alessandro Ghio; Davide Anguita; Sandro Ridella (Neural Networks, 2013)

Learning with few bits on small-scale devices: From regularization to energy efficiency. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (ESANN, 2014)

Out-of-Sample Error Estimation: The Blessing of High Dimensionality. - Luca Oneto; Alessandro Ghio; Sandro Ridella; Jorge Luis Reyes-Ortiz; Davide Anguita (ICDM Workshops, 2014)

Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (IJCNN, 2014)

Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (Pattern Recognit. Lett., 2014)

A Deep Connection Between the Vapnik-Chervonenkis Entropy and the Rademacher Complexity. - Davide Anguita; Alessandro Ghio; Luca Oneto; Sandro Ridella (IEEE Trans. Neural Networks Learn. Syst., 2014)

Shrinkage learning to improve SVM with hints. - Luca Oneto; Alessandro Ghio; Sandro Ridella; Davide Anguita (IJCNN, 2015)

Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters. - Luca Oneto; Alessandro Ghio; Sandro Ridella; Davide Anguita (IJCNN, 2015)

Fast convergence of extended Rademacher Complexity bounds. - Luca Oneto; Alessandro Ghio; Sandro Ridella; Davide Anguita (IJCNN, 2015)

Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency. - Luca Oneto; Alessandro Ghio; Sandro Ridella; Davide Anguita (Neurocomputing, 2015)

Local Rademacher Complexity: Sharper risk bounds with and without unlabeled samples. - Luca Oneto; Alessandro Ghio; Sandro Ridella; Davide Anguita (Neural Networks, 2015)

Fully Empirical and Data-Dependent Stability-Based Bounds. - Luca Oneto; Alessandro Ghio; Sandro Ridella; Davide Anguita (IEEE Trans. Cybern., 2015)

Tuning the Distribution Dependent Prior in the PAC-Bayes Framework based on Empirical Data. - Luca Oneto; Sandro Ridella; Davide Anguita (ESANN, 2016)

Tikhonov, Ivanov and Morozov regularization for support vector machine learning. - Luca Oneto; Sandro Ridella; Davide Anguita (Mach. Learn., 2016)

A local Vapnik-Chervonenkis complexity. - Luca Oneto; Davide Anguita; Sandro Ridella (Neural Networks, 2016)

Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates. - Luca Oneto; Alessandro Ghio; Sandro Ridella; Davide Anguita (Neural Process. Lett., 2016)

PAC-bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis. - Luca Oneto; Davide Anguita; Sandro Ridella (Pattern Recognit. Lett., 2016)

Learning Hardware-Friendly Classifiers Through Algorithmic Stability. - Luca Oneto; Sandro Ridella; Davide Anguita (ACM Trans. Embed. Comput. Syst., 2016)

Generalization Performances of Randomized Classifiers and Algorithms built on Data Dependent Distributions. - Luca Oneto; Sandro Ridella; Davide Anguita (ESANN, 2017)

Differential privacy and generalization: Sharper bounds with applications. - Luca Oneto; Sandro Ridella; Davide Anguita (Pattern Recognit. Lett., 2017)

Randomized learning: Generalization performance of old and new theoretically grounded algorithms. - Luca Oneto; Francesca Cipollini; Sandro Ridella; Davide Anguita (Neurocomputing, 2018)

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. - Luca Oneto; Nicolò Navarin; Michele Donini; Sandro Ridella; Alessandro Sperduti; Fabio Aiolli; Davide Anguita (IEEE Trans. Neural Networks Learn. Syst., 2018)

Local Rademacher Complexity Machine. - Luca Oneto; Sandro Ridella; Davide Anguita (Neurocomputing, 2019)

Improving the Union Bound: a Distribution Dependent Approach. - Luca Oneto; Sandro Ridella; Davide Anguita (ESANN, 2020)

The Benefits of Adversarial Defence in Generalisation. - Luca Oneto; Sandro Ridella; Davide Anguita (ESANN, 2021)

Distribution-Dependent Weighted Union Bound. - Luca Oneto; Sandro Ridella (Entropy, 2021)

Do We Really Need a New Theory to Understand the Double-Descent? - Luca Oneto; Sandro Ridella; Davide Anguita (ESANN, 2022)

The benefits of adversarial defense in generalization. - Luca Oneto; Sandro Ridella; Davide Anguita (Neurocomputing, 2022)

Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective. - Luca Oneto; Sandro Ridella; Davide Anguita (ESANN, 2023)

Do we really need a new theory to understand over-parameterization? - Luca Oneto; Sandro Ridella; Davide Anguita (Neurocomputing, 2023)

Towards algorithms and models that we can trust: A theoretical perspective. - Luca Oneto; Sandro Ridella; Davide Anguita (Neurocomputing, 2024)

Informed Machine Learning: Excess risk and generalization. - Luca Oneto; Sandro Ridella; Davide Anguita (Neurocomputing, 2025)