Massimiliano Pontil

发表

Structured Sparsity Models for Brain Decoding from fMRI Data

Luca Baldassarre, Janaina Mourão Miranda, Massimiliano Pontil, 2012, 2012 Second International Workshop on Pattern Recognition in NeuroImaging.

Fair Regression with Wasserstein Barycenters pdf

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

A New Machine Learning Approach to Fingerprint Classification

Yuan Yao, Gian Luca Marcialis, Fabio Roli, 2001, AI*IA.

On the Iteration Complexity of Hypergradient Computation

Massimiliano Pontil, Riccardo Grazzi, Luca Franceschi, 2020, ICML.

Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport pdf

Massimiliano Pontil, Alain Rakotomamonjy, Rosanna Turrisi, 2020, ArXiv.

A General Framework for Structured Sparsity via Proximal Optimization pdf

Luca Baldassarre, Massimiliano Pontil, Jean Morales, 2012, AISTATS.

A Spectral Regularization Framework for Multi-Task Structure Learning

Charles A. Micchelli, Massimiliano Pontil, Yiming Ying, 2007, NIPS.

Learning with dataset bias in latent subcategory models

Massimiliano Pontil, Vittorio Murino, Samuele Martelli, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Modelling transition dynamics in MDPs with RKHS embeddings pdf

Guy Lever, Arthur Gretton, Luca Baldassarre, 2012, ICML.

Mistake Bounds for Binary Matrix Completion

Mark Herbster, Massimiliano Pontil, Stephen Pasteris, 2016, NIPS.

Stability of Randomized Learning Algorithms

Massimiliano Pontil, Theodoros Evgeniou, André Elisseeff, 2005, J. Mach. Learn. Res..

Multi-task Averaging via Task Clustering

Massimiliano Pontil, David Martínez-Rego, 2013, SIMBAD.

Learning Multiple Tasks with Kernel Methods

Charles A. Micchelli, Massimiliano Pontil, Theodoros Evgeniou, 2005, J. Mach. Learn. Res..

Learning To Learn Around A Common Mean

Massimiliano Pontil, Carlo Ciliberto, Dimitris Stamos, 2018, NeurIPS.

Regularization Networks and Support Vector Machines

Tomaso A. Poggio, Massimiliano Pontil, Theodoros Evgeniou, 2000, Adv. Comput. Math..

People recognition and pose estimation in image sequences

Tomaso A. Poggio, Massimiliano Pontil, Chikahito Nakajima, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction

Massimiliano Pontil, Carlo Ciliberto, Dimitris Stamos, 2019, ICML.

Transfer learning in a heterogeneous environment

Massimiliano Pontil, Andreas Maurer, M. Pontil, 2012, 2012 3rd International Workshop on Cognitive Information Processing (CIP).

Empirical Bernstein Bounds and Sample-Variance Penalization

Massimiliano Pontil, Andreas Maurer, M. Pontil, 2009, COLT.

Multi-Task Feature Learning

Massimiliano Pontil, Theodoros Evgeniou, Andreas Argyriou, 2006, NIPS.

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

John Shawe-Taylor, Janaina Mourão Miranda, Massimiliano Pontil, 2018, NeuroImage.

Direct Aspect-Based 3D Object Recognition

Massimiliano Pontil, Alessandro Verri, 1997, ICIAP.

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance

Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto, 2018, NeurIPS.

Wide coverage natural language processing using kernel methods and neural networks for structured data

Paolo Frasconi, Massimiliano Pontil, Fabrizio Costa, 2005, Pattern Recognit. Lett..

Conditional mean embeddings as regressors

Guy Lever, Arthur Gretton, Luca Baldassarre, 2012, ICML.

Incorporating Additional Constraints in Sparse Estimation

Luca Baldassarre, Massimiliano Pontil, Jean Morales, 2012 .

Fitting Spectral Decay with the k-Support Norm

Massimiliano Pontil, Dimitris Stamos, Andrew M. McDonald, 2016, AISTATS.

Meta-learning with Stochastic Linear Bandits pdf

Alessandro Lazaric, Massimiliano Pontil, Leonardo Cella, 2020, ICML.

Distributed variance regularized Multitask Learning

John Shawe-Taylor, Massimiliano Pontil, Michele Donini, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Online Model Selection: a Rested Bandit Formulation pdf

Claudio Gentile, Massimiliano Pontil, Leonardo Cella, 2020, ArXiv.

Quantum machine learning: a classical perspective

Simone Severini, Andrea Rocchetto, Mark Herbster, 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning

Massimiliano Pontil, Bernardino Romera-Paredes, Andreas Maurer, 2014, COLT.

On Sparsity Inducing Regularization Methods for Machine Learning

Charles A. Micchelli, Luca Baldassarre, Massimiliano Pontil, 2013, Empirical Inference.

From Margins to Probabilities in Multiclass Learning Problems

Paolo Frasconi, Andrea Passerini, Massimiliano Pontil, 2002, ECAI.

Online learning over graphs

Mark Herbster, Massimiliano Pontil, Lisa Wainer, 2005, ICML.

A Function Representation for Learning in Banach Spaces

Charles A. Micchelli, Massimiliano Pontil, C. Micchelli, 2004, COLT.

A Family of Penalty Functions for Structured Sparsity

Charles A. Micchelli, Massimiliano Pontil, Jean Morales, 2010, NIPS.

Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs

Darwin G. Caldwell, Massimiliano Pontil, Luca Franceschi, 2018, IEEE Robotics and Automation Letters.

Distance-Based Regularisation of Deep Networks for Fine-Tuning pdf

Massimiliano Pontil, Timothy M. Hospedales, Henry Gouk, 2020, ICLR.

Error analysis for online gradient descent algorithms in reproducing kernel Hilbert spaces

Massimiliano Pontil, Y Ying, D-X Zhou, 2006 .

From regression to classification in support vector machines

Massimiliano Pontil, Theodoros Evgeniou, Ryan M. Rifkin, 1999, ESANN.

Machine Learning Techniques for Biometrics

Francesca Odone, Massimiliano Pontil, Alessandro Verri, 2009, Handbook of Remote Biometrics.

A Bridge Between Hyperparameter Optimization and Larning-to-learn pdf

Paolo Frasconi, Massimiliano Pontil, Luca Franceschi, 2017, ArXiv.

Optimal kernel choice for large-scale two-sample tests

Sivaraman Balakrishnan, Kenji Fukumizu, Arthur Gretton, 2012, NIPS.

Bilevel learning of the Group Lasso structure

Massimiliano Pontil, Saverio Salzo, Jordan Frécon, 2018, NeurIPS.

Convex multi-task feature learning

Massimiliano Pontil, Theodoros Evgeniou, Andreas Argyriou, 2008, Machine Learning.

Recognizing 3-D Objects with Linear Support Vector Machines

Massimiliano Pontil, Alessandro Verri, Stefano Rogai, 1998, ECCV.

Comparing Convolution Kernels and Recursive Neural Networks for Learning Preferences on Structured Data

Paolo Frasconi, Massimiliano Pontil, Fabrizio Costa, 2003 .

Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers

Massimiliano Pontil, André Elisseeff, Theodoros Evgeniou, 2004, Machine Learning.

Regularization and statistical learning theory for data analysis

Massimiliano Pontil, Alessandro Verri, Theodoros Evgeniou, 2002 .

Combining Graph Laplacians for Semi-Supervised Learning

Mark Herbster, Massimiliano Pontil, Andreas Argyriou, 2005, NIPS.

Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning pdf

Paolo Frasconi, Massimiliano Pontil, Riccardo Grazzi, 2018, ArXiv.

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

John Shawe-Taylor, Massimiliano Pontil, Andreas J. Fallgatter, 2018 .

A note on different covering numbers in learning theory

Massimiliano Pontil, M. Pontil, 2003, J. Complex..

Support Vector Machines: Theory and Applications

Massimiliano Pontil, Theodoros Evgeniou, M. Pontil, 2001, Machine Learning and Its Applications.

$K$ -Dimensional Coding Schemes in Hilbert Spaces

Massimiliano Pontil, Augusto Maurer, M. Pontil, 2010, IEEE Transactions on Information Theory.

Learning with optimal interpolation norms

Patrick L. Combettes, Charles A. Micchelli, Massimiliano Pontil, 2016, Numerical Algorithms.

Learning Convex Combinations of Continuously Parameterized Basic Kernels

Charles A. Micchelli, Massimiliano Pontil, Andreas Argyriou, 2005, COLT.

Transfer learning to account for idiosyncrasy in face and body expressions

Massimiliano Pontil, M. S. Hane Aung, Nadia Bianchi-Berthouze, 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

Estimating weighted areas under the ROC curve

Massimiliano Pontil, Andreas Maurer, M. Pontil, 2020, NeurIPS.

Bilevel Programming for Hyperparameter Optimization and Meta-Learning pdf

Paolo Frasconi, Massimiliano Pontil, Luca Franceschi, 2018, ICML.

Learning Discrete Structures for Graph Neural Networks

Massimiliano Pontil, Mathias Niepert, Luca Franceschi, 2019, ICML.

Reexamining Low Rank Matrix Factorization for Trace Norm Regularization pdf

Massimiliano Pontil, Carlo Ciliberto, Dimitris Stamos, 2017, Mathematics in Engineering.

A Unified Framework for Regularization Networks and Support Vector Machines

Tomaso Poggio, Massimiliano Pontil, Theodoros Evgeniou, 1999 .

Forward and Reverse Gradient-Based Hyperparameter Optimization

Paolo Frasconi, Massimiliano Pontil, Luca Franceschi, 2017, ICML.

Categorization by Learning and Combining Object Parts

Thomas Serre, Tomaso A. Poggio, Massimiliano Pontil, 2001, NIPS.

General Fair Empirical Risk Minimization

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

Structured Sparsity and Generalization pdf

Massimiliano Pontil, Andreas Maurer, M. Pontil, 2011, J. Mach. Learn. Res..

On the Vgamma Dimension for Regression in Reproducing Kernel Hilbert Spaces

Massimiliano Pontil, Theodoros Evgeniou, M. Pontil, 1999, ALT.

Multilinear Multitask Learning

Massimiliano Pontil, M. S. Hane Aung, Nadia Bianchi-Berthouze, 2013, ICML.

A New Convex Relaxation for Tensor Completion

Massimiliano Pontil, Bernardino Romera-Paredes, M. Pontil, 2013, NIPS.

Automatic Recognition of Facial Expressions

Massimiliano Pontil, Andreas Argyriou, Nadia Bianchi-Berthouze, 2012 .

Spectral k-Support Norm Regularization

Massimiliano Pontil, Dimitris Stamos, Andrew M. McDonald, 2014, NIPS.

On the Iteration Complexity of Hypergradient Computations

Massimiliano Pontil, Riccardo Grazzi, Luca Franceschi, 2020, ICML 2020.

PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors

Luca Oneto, Massimiliano Pontil, Michele Donini, 2019, ESANN.

Feature Selection for SVMs

Sayan Mukherjee, Jason Weston, Tomaso A. Poggio, 2000, NIPS.

Learning local metrics from pairwise similarity data

Massimiliano Pontil, Yiming Ying, Stéphane Gentric, 2018, Pattern Recognit..

Large Margin Local Metric Learning

Massimiliano Pontil, Yiming Ying, Stéphane Gentric, 2014, ECCV.

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization pdf

Massimiliano Pontil, Carlo Ciliberto, Riccardo Grazzi, 2019, ICML.

Uniform concentration and symmetrization for weak interactions

Massimiliano Pontil, Andreas Maurer, 2019, COLT.

Empirical bounds for functions with weak interactions

Massimiliano Pontil, Andreas Maurer, 2018, COLT.

Regularizers for structured sparsity

Charles A. Micchelli, Massimiliano Pontil, Jean Morales, 2010, Adv. Comput. Math..

Incremental Learning-to-Learn with Statistical Guarantees pdf

Massimiliano Pontil, Carlo Ciliberto, Dimitris Stamos, 2018, UAI.

On Learning Vector-Valued Functions

Charles A. Micchelli, Massimiliano Pontil, C. Micchelli, 2005, Neural Computation.

Reproducing kernels and regularization methods in machine learning

Massimiliano Pontil, 2003, ESANN.

Regularized multi--task learning

Massimiliano Pontil, Theodoros Evgeniou, M. Pontil, 2004, KDD.

Bounds for Vector-Valued Function Estimation pdf

Massimiliano Pontil, Andreas Maurer, 2016, ArXiv.

Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces

Massimiliano Pontil, Andreas Maurer, M. Pontil, 2008, ALT.

Unsupervised Cross-Dataset Transfer Learning for Person Re-identification

Shaogang Gong, Tao Xiang, Tiejun Huang, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Machine Learning Strategies for Complex Tasks

Colin Campbell, Massimiliano Pontil, Theodoros Evgeniou, 2011 .

The Benefit of Multitask Representation Learning

Massimiliano Pontil, Bernardino Romera-Paredes, Andreas Maurer, 2015, J. Mach. Learn. Res..

A Perturbation Resilient Framework for Unsupervised Learning pdf

Massimiliano Pontil, Andrea Paudice, Andreas Maurer, 2020, ArXiv.

On Hyperparameter Optimization in Learning Systems

Paolo Frasconi, Massimiliano Pontil, Luca Franceschi, 2017, ICLR.

Modeling transition dynamics in MDPs with RKHS embeddings of conditional distributions pdf

Guy Lever, Arthur Gretton, Luca Baldassarre, 2011, ArXiv.

An Empirical Study of Geographic User Activity Patterns in Foursquare

Cecilia Mascolo, Massimiliano Pontil, Anastasios Noulas, 2011, ICWSM.

Online Gradient Descent Learning Algorithms

Massimiliano Pontil, Yiming Ying, M. Pontil, 2008, Found. Comput. Math..

A multimodal multiple kernel learning approach to Alzheimer's disease detection

John Shawe-Taylor, Janaina Mourão Miranda, Massimiliano Pontil, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

A Note on Support Vector Machine Degeneracy

Massimiliano Pontil, Alessandro Verri, Ryan M. Rifkin, 1999, ALT.

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm

Massimiliano Pontil, Carlo Ciliberto, Saverio Salzo, 2019, NeurIPS.

Marthe: Scheduling the Learning Rate Via Online Hypergradients

Paolo Frasconi, Massimiliano Pontil, Luca Franceschi, 2019, IJCAI.

On different ensembles of kernel machines

Shun-ichi Amari, Massimiliano Pontil, Hiroyuki Nakahara, 2003, ESANN.

Taking Advantage of Sparsity in Multi-Task Learning

Massimiliano Pontil, Sara A. van de Geer, Alexandre B. Tsybakov, 2009, COLT.

Lower Bounds for Sparse Coding

Luca Baldassarre, Massimiliano Pontil, Andreas Maurer, 2015 .

Multiple Instance Learning for Emotion Recognition Using Physiological Signals

Luca Romeo, Massimiliano Pontil, Lucia Pepa, 2022, IEEE Transactions on Affective Computing.

Statistical Learning Theory: A Primer

Tomaso A. Poggio, Massimiliano Pontil, Theodoros Evgeniou, 2000, International Journal of Computer Vision.

Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits pdf

Massimiliano Pontil, Alexandre B. Tsybakov, Arya Akhavan, 2020, NeurIPS.

Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines

Yuan Yao, Gian Luca Marcialis, Fabio Roli, 2003, Pattern Recognit..

A Tale of Many Cities: Universal Patterns in Human Urban Mobility

Cecilia Mascolo, Massimiliano Pontil, Renaud Lambiotte, 2011, PloS one.

An Algorithm for Transfer Learning in a Heterogeneous Environment

Massimiliano Pontil, Andreas Maurer, Andreas Argyriou, 2008, ECML/PKDD.

Learning Preference Relations from Data

Massimiliano Pontil, Theodoros Evgeniou, M. Pontil, 2002, WIRN.

A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion

Massimiliano Pontil, Raman Arora, Luca Franceschi, 2017, INTERSPEECH.

Convergence Properties of Stochastic Hypergradients pdf

Massimiliano Pontil, Riccardo Grazzi, Saverio Salzo, 2020, AISTATS.

Component-based face detection

Thomas Serre, Tomaso A. Poggio, Massimiliano Pontil, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

Conditional mean embeddings as regressors - supplementary pdf

Guy Lever, Arthur Gretton, Luca Baldassarre, 2012, ArXiv.

On Tuning Hyper-Parameters of Multiclass Margin Classifiers

Paolo Frasconi, Andrea Passerini, Massimiliano Pontil, 2002 .

Efficient First Order Methods for Linear Composite Regularizers pdf

Lixin Shen, Yuesheng Xu, Charles A. Micchelli, 2011, ArXiv.

Algorithmic Stability and Model Selection for Bagging using Small Sub-samples.

Massimiliano Pontil, Theodoros Evgeniou, André Elisseeff, 2001, NIPS 2001.

Bounds on the Generalization Performance of Kernel Machine Ensembles

Tomaso A. Poggio, Massimiliano Pontil, Theodoros Evgeniou, 2000, ICML.

A Simple Algorithm for Learning Stable Machines

Massimiliano Pontil, Theodoros Evgeniou, André Elisseeff, 2002, ECAI.

Excess risk bounds for multitask learning with trace norm regularization pdf

Massimiliano Pontil, Andreas Maurer, M. Pontil, 2012, COLT.

Regret Bounds for Lifelong Learning pdf

Pierre Alquier, Massimiliano Pontil, The Tien Mai, 2016, AISTATS.

Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks

Cecilia Mascolo, Massimiliano Pontil, Anastasios Noulas, 2011, The Social Mobile Web.

Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152)

Gunnar Rätsch, Trevor Darrell, Marius Kloft, 2015, Dagstuhl Reports.

Efficient Tensor Kernel methods for sparse regression pdf

Marcello Pelillo, Massimiliano Pontil, Saverio Salzo, 2020, ArXiv.

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Massimiliano Pontil, Cédric Archambeau, David T. Jones, 2009, BMC Bioinformatics.

Online-Within-Online Meta-Learning

Massimiliano Pontil, Carlo Ciliberto, Dimitris Stamos, 2019, NeurIPS.

Properties of Support Vector Machines

Massimiliano Pontil, Alessandro Verri, A. Verri, 1998, Neural Computation.

Massimiliano Pontil, Andreas Argyriou, Nadia Bianchi-Berthouze, 2012, AISTATS.

Prediction on a Graph with a Perceptron

Mark Herbster, Massimiliano Pontil, 2006, NIPS.

When is there a representer theorem? Vector versus matrix regularizers

Charles A. Micchelli, Massimiliano Pontil, Andreas Argyriou, 2008, J. Mach. Learn. Res..

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

Massimiliano Pontil, Daniel W. A. Buchan, David T. Jones, 2012, Bioinform..

Fast Prediction on a Tree

Mark Herbster, Massimiliano Pontil, Sergio Rojas Galeano, 2008, NIPS.

Computation of Feynman Diagrams with MATHEMATICA

Massimiliano Pontil, 1994 .

Approximating Hamiltonian dynamics with the Nyström method pdf

Simone Severini, Andrea Rocchetto, Alessandro Rudi, 2018, Quantum.

New results on error correcting output codes of kernel machines

Paolo Frasconi, Andrea Passerini, Massimiliano Pontil, 2004, IEEE Transactions on Neural Networks.

Learning the Kernel Function via Regularization

Charles A. Micchelli, Massimiliano Pontil, C. Micchelli, 2005, J. Mach. Learn. Res..

Study and Application of Statistical Learning Theory

Massimiliano Pontil, 1999 .

Full-body person recognition system

Tomaso A. Poggio, Massimiliano Pontil, Bernd Heisele, 2003, Pattern Recognit..

Learning Fair and Transferable Representations pdf

Luca Oneto, Massimiliano Pontil, Andreas Maurer, 2019, ArXiv.

On Spectral Learning

Charles A. Micchelli, Massimiliano Pontil, Andreas Argyriou, 2010, J. Mach. Learn. Res..

Sparsity Is Better with Stability: Combining Accuracy and Stability for Model Selection in Brain Decoding

Luca Baldassarre, Massimiliano Pontil, Janaina Mourão-Miranda, 2017, Front. Neurosci..

Support Vector Machines with Clustering for Training with Very Large Datasets

Massimiliano Pontil, Theodoros Evgeniou, 2002, SETN.

Structured sparse-low rank matrix factorization for the EEG inverse problem

Antonio Artés-Rodríguez, Massimiliano Pontil, Jair Montoya-Martínez, 2014, 2014 4th International Workshop on Cognitive Information Processing (CIP).

A Note on the Generalization Performance of Kernel Classifiers with Margin

Massimiliano Pontil, Theodoros Evgeniou, 2000, ALT.

Fast and Continuous Foothold Adaptation for Dynamic Locomotion through Convolutional Neural Networks pdf

Darwin G. Caldwell, Massimiliano Pontil, Luca Franceschi, 2018, ArXiv.

A Uniform Lower Error Bound for Half-Space Learning

Massimiliano Pontil, Andreas Maurer, 2008, ALT.

Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning

Tao Xiang, Tiejun Huang, Yonghong Tian, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Short Review of Statistical Learning Theory

Massimiliano Pontil, M. Pontil, 2002, WIRN.

Kernels for Multi--task Learning

Charles A. Micchelli, Massimiliano Pontil, C. Micchelli, 2004, NIPS.

A regularized matrix factorization approach to induce structured sparse-low-rank solutions in the EEG inverse problem

Lars Kai Hansen, Antonio Artés-Rodríguez, Massimiliano Pontil, 2014, EURASIP J. Adv. Signal Process..

Maintenance Training of Electric Power Facilities Using Object Recognition by SVM

Massimiliano Pontil, Chikahito Nakajima, 2002, SVM.

Massimiliano Pontil, Stéphane Gentric, Sylvain Colin, 2016, ICPRAM.

Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines RID G-2944-2010

Gian Luca Marcialis, Fabio Roli, Paolo Frasconi, 2003 .

Sparse coding for multitask and transfer learning pdf

Massimiliano Pontil, Bernardino Romera-Paredes, Andreas Maurer, 2012, ICML.

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning pdf

Massimiliano Pontil, Carlo Ciliberto, Giulia Denevi, 2020, NeurIPS.

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

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

Learning with Kernel Machines and their Ensembles

Massimiliano Pontil, Theodoros Evgeniou, 2000 .

A DC-programming algorithm for kernel selection

Charles A. Micchelli, Massimiliano Pontil, Andreas Argyriou, 2006, ICML.

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning

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

Fingerprint Classification with Combinations of Support Vector Machines

Yuan Yao, Paolo Frasconi, Massimiliano Pontil, 2001, AVBPA.

Kernel conditional graphical models

Zoubin Ghahramani, Massimiliano Pontil, F Perez Cruz, 2007 .

Image representations for object detection using kernel classifiers

Tomaso A. Poggio, Massimiliano Pontil, C. Papageorgiou, 2000 .

Support vector machines vs multi-layer perceptrons in particle identification

Massimiliano Pontil, Alessandro Verri, M. Pallavicini, 1999, ESANN.

Generalization Properties of Optimal Transport GANs with Latent Distribution Learning pdf

Massimiliano Pontil, Carlo Ciliberto, Giulia Luise, 2020, ArXiv.

Conditional Graphical Models

Zoubin Ghahramani, Massimiliano Pontil, Fernando Perez-Cruz, 2007 .

Object recognition and detection by a combination of support vector machine and rotation invariant phase only correlation

Tomaso A. Poggio, Massimiliano Pontil, Norihiko Itoh, 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

An introduction to learning with reproducing kernel Hilbert spaces

Massimiliano Pontil, 2003 .

Support vector machines: a large scale QP

Massimiliano Pontil, Alessandro Verri, Stefano Rogai, 1998 .

Online Parameter-Free Learning of Multiple Low Variance Tasks

Massimiliano Pontil, Dimitris Stamos, Giulia Denevi, 2020, UAI.

Empirical Risk Minimization under Fairness Constraints

Shai Ben-David, John Shawe-Taylor, Luca Oneto, 2018, NeurIPS.

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).

Online Prediction on Large Diameter Graphs

Guy Lever, Mark Herbster, Massimiliano Pontil, 2008, NIPS.

People Recognition in Image Sequences by Supervised Learning

Massimiliano Pontil, Tomaso Poggio, Bernd Heisele, 2000 .

Entropy conditions for Lr-convergence of empirical processes

Massimiliano Pontil, Ernesto De Vito, Andrea Caponnetto, 2009, Adv. Comput. Math..

Universal Multi-Task Kernels

Charles A. Micchelli, Massimiliano Pontil, Yiming Ying, 2008, J. Mach. Learn. Res..

Face Detection in Still Gray Images

Massimiliano Pontil, Tomaso Poggio, Bernd Heisele, 2000 .

Structured sparsity regularization approach to the EEG inverse problem

Lars Kai Hansen, Antonio Artés-Rodríguez, Massimiliano Pontil, 2012, 2012 3rd International Workshop on Cognitive Information Processing (CIP).

Support Vector Machines for 3D Object Recognition

Massimiliano Pontil, Alessandro Verri, A. Verri, 1998, IEEE Trans. Pattern Anal. Mach. Intell..

Taking Advantage of Multitask Learning for Fair Classification

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

Predicting a switching sequence of graph labelings

Mark Herbster, Massimiliano Pontil, Stephen Pasteris, 2015, J. Mach. Learn. Res..

New Perspectives on k-Support and Cluster Norms pdf

Massimiliano Pontil, Dimitris Stamos, Andrew M. McDonald, 2014, J. Mach. Learn. Res..

Consistent Multitask Learning with Nonlinear Output Relations

Lorenzo Rosasco, Alessandro Rudi, Massimiliano Pontil, 2017, NIPS.

On the Noise Model of Support Vector Machines Regression

Massimiliano Pontil, Federico Girosi, Sayan Mukherjee, 2000, ALT.

Image Representations and Feature Selection for Multimedia Database Search

Tomaso A. Poggio, Massimiliano Pontil, Theodoros Evgeniou, 2003, IEEE Trans. Knowl. Data Eng..

Feature space perspectives for learning the kernel

Charles A. Micchelli, Massimiliano Pontil, 2006, Machine Learning.

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification

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

Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm pdf

Paolo Frasconi, Massimiliano Pontil, Luca Franceschi, 2019, ArXiv.

Marthe: Scheduling the Learning Rate Via Online Hypergradients

Paolo Frasconi, Massimiliano Pontil, Luca Franceschi, 2019, IJCAI.

Unveiling Groups of Related Tasks in Multi - Task Learning

Massimiliano Pontil, Saverio Salzo, Jordan Frécon, 2021, 2020 25th International Conference on Pattern Recognition (ICPR).

Robust Unsupervised Learning via L-statistic Minimization

Massimiliano Pontil, Andrea Paudice, Andreas Maurer, 2021, ICML.

Best Model Identification: A Rested Bandit Formulation

Claudio Gentile, Massimiliano Pontil, Leonardo Cella, 2021, ICML.

Multitask Online Mirror Descent pdf

Massimiliano Pontil, Andrea Paudice, Pierre Laforgue, 2021, ArXiv.

Distributed Zero-Order Optimization under Adversarial Noise

Massimiliano Pontil, Alexandre B. Tsybakov, Arya Akhavan, 2021, NeurIPS.

Multi-task and meta-learning with sparse linear bandits

Massimiliano Pontil, Leonardo Cella, M. Pontil, 2021, UAI.

The Role of Global Labels in Few-Shot Classification and How to Infer Them pdf

Massimiliano Pontil, Ruohan Wang, Carlo Ciliberto, 2021, NeurIPS.

Distance-Based Regularisation of Deep Networks for Fine-Tuning pdf

Massimiliano Pontil, Timothy M. Hospedales, Henry Gouk, 2021, ICLR.

Conditional Meta-Learning of Linear Representations pdf

Massimiliano Pontil, Carlo Ciliberto, Giulia Denevi, 2021, ArXiv.

Convergence Properties of Stochastic Hypergradients

Massimiliano Pontil, Riccardo Grazzi, Saverio Salzo, 2021, AISTATS.

Some Hoeffding- and Bernstein-type Concentration Inequalities pdf

Massimiliano Pontil, Andreas Maurer, 2021, ArXiv.

Deep Convolutional Terrain Assessment for Visual Reactive Footstep Correction on Dynamic Legged Robots

Darwin G. Caldwell, Massimiliano Pontil, Luca Franceschi, 2018 .

Correction: A Tale of Many Cities: Universal Patterns in Human Urban Mobility

Cecilia Mascolo, Massimiliano Pontil, Renaud Lambiotte, 2012, PLoS ONE.

Prospective motor control obeys to idiosyncratic strategies in autism

Luca Romeo, Massimiliano Pontil, Cristina Becchio, 2018, Scientific Reports.

Action Observation Areas Represent Intentions From Subtle Kinematic Features

Massimiliano Pontil, Franco Cauda, Tommaso Costa, 2018, Cerebral cortex.