M. Pontil

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

Optimal kernel choice for large-scale two-sample tests

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

Regularization Networks and Support Vector Machines

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

From Regression to Classication in Support Vector Machines

M. Pontil, R. Rifkin, T. Evgeniou, 1999 .

Regularized multi--task learning

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

Support Vector Machines for 3D Object Recognition

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

Leave-one-out error and stability of learning algorithms with applications

M. Pontil, 2002 .

The Benefit of Multitask Representation Learning

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

Forward and Reverse Gradient-Based Hyperparameter Optimization

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

Kernels for Multi--task Learning

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

Learning Multiple Tasks with Kernel Methods

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

Conditional mean embeddings as regressors

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

Feature Selection for SVMs

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

A New Convex Relaxation for Tensor Completion

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

Quantum machine learning: a classical perspective

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

Approximating Hamiltonian dynamics with the Nyström method pdf

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

Modelling transition dynamics in MDPs with RKHS embeddings pdf

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

On Learning Vector-Valued Functions

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

Stability of Randomized Learning Algorithms

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

Online Gradient Descent Learning Algorithms

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

Image Representations and Feature Selection for Multimedia Database Search

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

On the Noise Model of Support Vector Machines Regression

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

A DC-programming algorithm for kernel selection

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

A Spectral Regularization Framework for Multi-Task Structure Learning

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

A Unified Framework for Regularization Networks and Support Vector Machines

Tomaso Poggio, Massimiliano Pontil, Theodoros Evgeniou, 1999 .

Online Prediction on Large Diameter Graphs

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

A Function Representation for Learning in Banach Spaces

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

On the Vgamma Dimension for Regression in Reproducing Kernel Hilbert Spaces

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

Face Detection in Still Gray Images

Massimiliano Pontil, Tomaso Poggio, Bernd Heisele, 2000 .

Empirical Risk Minimization under Fairness Constraints

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

An Efficient Method to Impose Fairness in Linear Models

J. Shawe-Taylor, S. Ben-David, M. Pontil, 2017 .

Properties of Support Vector Machines

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

Regret Bounds for Lifelong Learning pdf

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

When is there a representer theorem? Vector versus matrix regularizers

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

The Nearest-Neighbor Technique for particle identification

A. Verri, M. Pontil, M. Pallavicini, 1998 .

Estimating weighted areas under the ROC curve

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

YPERPARAMETER O PTIMIZATION IN L EARNING S YSTEMS

P. Frasconi, M. Pontil, Luca Franceschi, 2017 .

Paper No . 171 A unified framework for Regularization Networks and Support Vector Machines

T. Poggio, M. Pontil, T. Evgeniou, 1998 .

A ug 2 01 7 Learning with Optimal Interpolation Norms ∗

C. Micchelli, P. L. Combettes, M. Pontil, 2017 .

Error bounds for learning the kernel

Ding-Xuan Zhou, C. Micchelli, M. Pontil, 2016 .

Learning with Optimal Interpolation Norms: Properties and Algorithms

C. Micchelli, P. L. Combettes, M. Pontil, 2016 .

Stability of Randomized Learning Algorithms with an Application to Bootstrap Methods

A. Elisseeff, M. Pontil, 2022 .

Efficient First Order Methods for Linear Composite Regularizers pdf

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

Support Vector Machines: Theory and Applications

M. Pontil, T. Evgeniou, 2001, Machine Learning and Its Applications.

Large Margin Local Metric Learning

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

Inferring Interests from Mobility and Social Interactions

M. Pontil, Mirco Musolesi, C. Mascolo, 2009 .

Regularized Robust Portfolio Estimation

M. Pontil, T. Evgeniou, Diomedis D. Spinellis, 2013 .

O N S PECTRAL L EARNING On Spectral Learning

C. Micchelli, M. Pontil, Andreas Argyriou, 2013 .

A pr 2 01 8 Approximating Hamiltonian dynamics with the Nyström method

M. Pontil, S. Severini, C. Ciliberto, 2018 .

Kernel conditional graphical models

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

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance

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

People Recognition in Image Sequences by Supervised Learning

Massimiliano Pontil, Tomaso Poggio, Bernd Heisele, 2000 .

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 .

Automatic Recognition of Facial Expressions

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

A gradient estimator via L1-randomization for online zero-order optimization with two point feedback

A. Tsybakov, Arya Akhavan, M. Pontil, 2022, 2205.13910.

Group Meritocratic Fairness in Linear Contextual Bandits

Arya Akhavan, M. Pontil, Leonardo Cella, 2022, ArXiv.

Support Vector Machines: A Large Scale QP Problem*

A. Verri, M. Pontil, Stefano Rogai, 1998 .

Distribution Regression with Sliced Wasserstein Kernels

M. Pontil, C. Ciliberto, D. Meunier, 2022, ICML.

Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport

M. Pontil, A. Rakotomamonjy, Rémi Flamary, 2020, UAI.

Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces

M. Pontil, L. Rosasco, Andreas Maurer, 2022, ArXiv.

Identifying the signature of prospective motor control in children with autism

M. Pontil, S. Panzeri, C. Becchio, 2020, Scientific Reports.

Marthe: Scheduling the Learning Rate Via Online Hypergradients

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

On Hyperparameter Optimization in Learning Systems

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

High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise

M. Pontil, Saverio Salzo, Andrea Paudice, 2022, 2208.08567.

Bregman Neural Networks

M. Pontil, Jordan Frécon, Saverio Salzo, 2022, ICML.

A gradient estimator via L1-randomization for online zero-order optimization with two point feedback

A. Tsybakov, Arya Akhavan, M. Pontil, 2022, NeurIPS.

Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start

M. Pontil, Saverio Salzo, Riccardo Grazzi, 2022, J. Mach. Learn. Res..

Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces

M. Pontil, L. Rosasco, Andreas Maurer, 2022, ArXiv.

Implicit kernel meta-learning using kernel integral forms

M. Pontil, C. Ciliberto, Johannes Falk, 2022, UAI.

Schedule-Robust Online Continual Learning

M. Pontil, Marco Ciccone, C. Ciliberto, 2022, ArXiv.

Multitask Online Mirror Descent

N. Cesa-Bianchi, M. Pontil, Andrea Paudice, 2021, Trans. Mach. Learn. Res..

A meta-learning BCI for estimating decision confidence

M. Pontil, L. Citi, R. Poli, 2022, Journal of neural engineering.

On the Iteration Complexity of Hypergradient Computations

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

Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity

M. Pontil, Saverio Salzo, V. Kostic, 2022, ICML.

Group Meritocratic Fairness in Linear Contextual Bandits

Arya Akhavan, M. Pontil, Leonardo Cella, 2022, ArXiv.

Meta-Learning Representations with Contextual Linear Bandits

M. Pontil, Karim Lounici, Leonardo Cella, 2022 .

Decoding social decisions from movement kinematics

M. Pontil, S. Panzeri, A. Sanfey, 2022, iScience.

Journal Pre-proof Decoding social decisions from movement kinematics

M. Pontil, A. Sanfey, C. Becchio, 2022 .

Regularizers for structured sparsity

C. Micchelli, M. Pontil, Jean Morales, 2011, Advances in Computational Mathematics.

Group Meritocratic Fairness in Linear Contextual Bandits

Arya Akhavan, M. Pontil, Leonardo Cella, 2022, ArXiv.

Robust Meta-Representation Learning via Global Label Inference and Classification

M. Pontil, C. Ciliberto, Ruohan Wang, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

On different ensembles of kernel machines

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

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

A. Tsybakov, Arya Akhavan, M. Pontil, 2020, NeurIPS.

Lower Bounds for Sparse Coding

Luca Baldassarre, Massimiliano Pontil, Andreas Maurer, 2015 .

Multi-task Learning

Massimiliano Pontil, M. Pontil, 2020, Transfer Learning.

Learning Discrete Structures for Graph Neural Networks

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

A Simple Algorithm for Learning Stable Machines

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

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..

Statistical Learning Theory: A Primer

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

A Short Review of Statistical Learning Theory

Massimiliano Pontil, M. Pontil, 2002, WIRN.

Learning Preference Relations from Data

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

Regularization and statistical learning theory for data analysis

Massimiliano Pontil, Alessandro Verri, Theodoros Evgeniou, 2002 .

Concentration inequalities under sub-Gaussian and sub-exponential conditions

M. Pontil, Andreas Maurer, 2021, NeurIPS.

On the Iteration Complexity of Hypergradient Computation

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

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

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

On Sparsity Inducing Regularization Methods for Machine Learning

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

Convergence Properties of Stochastic Hypergradients pdf

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

Bilevel Programming for Hyperparameter Optimization and Meta-Learning pdf

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

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..

Characterizing metastable states with the help of machine learning

M. Pontil, M. Parrinello, L. Bonati, 2022, Journal of chemical theory and computation.

Fitting Spectral Decay with the k-Support Norm

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

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..

General Fair Empirical Risk Minimization

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

Taking Advantage of Multitask Learning for Fair Classification

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

New results on error correcting output codes of kernel machines

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

Machine Learning Strategies for Complex Tasks

Colin Campbell, Massimiliano Pontil, Theodoros Evgeniou, 2011 .

Conditional Graphical Models

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

Full-body person recognition system

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

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

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

An Empirical Study of Geographic User Activity Patterns in Foursquare

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

Support Vector Machines: Theory and Applications

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

Generalization Properties of Optimal Transport GANs with Latent Distribution Learning pdf

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

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm

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

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

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

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

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

Distributed Zero-Order Optimization under Adversarial Noise

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

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.

Universal Multi-Task Kernels

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

A Family of Penalty Functions for Structured Sparsity

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

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification

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

Transfer learning in a heterogeneous environment

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

Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces

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

Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines

Daniel W. A. Buchan, David T. Jones, M. Pontil, 2011, PloS one.

Robust Unsupervised Learning via L-statistic Minimization

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

A Perturbation Resilient Framework for Unsupervised Learning pdf

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

Spectral k-Support Norm Regularization

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

Conditional mean embeddings as regressors - supplementary pdf

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

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

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

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

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

Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces

M. Pontil, L. Rosasco, Andreas Maurer, 2022, NeurIPS.

Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues

M. Pontil, Karim Lounici, P. Novelli, 2023, ArXiv.

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction

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

Excess risk bounds for multitask learning with trace norm regularization pdf

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

Predicting a switching sequence of graph labelings

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

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

Bilevel learning of the Group Lasso structure

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

Multi-task and Transfer Learning

Massimiliano Pontil, M. Pontil, 2013 .

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.

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

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

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

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

Structured Sparsity and Generalization pdf

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

Sparse coding for multitask and transfer learning pdf

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

Taking Advantage of Sparsity in Multi-Task Learning

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

Best Of NIPS 2005: Highlights on the 'Inductive Transfer : 10 Years Later' Workshop

Massimiliano Pontil, Rich Caruana, Prasad Tadepalli, 2006 .

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

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

Online learning over graphs

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

Machine Learning for Cultural Heritage: A Survey

M. Pontil, Stuart James, A. Traviglia, 2020, Pattern Recognit. Lett..

Multiple Instance Learning for Emotion Recognition Using Physiological Signals

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

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

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

Bounds on the Generalization Performance of Kernel Machine Ensembles

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

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

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

Fair Regression with Wasserstein Barycenters pdf

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

$K$ -Dimensional Coding Schemes in Hilbert Spaces

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

Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines

Daniel W. A. Buchan, David T. Jones, M. Pontil, 2011, PloS one.

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.

Multilinear Multitask Learning

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

On Spectral Learning

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

Inferring Interests from Mobility and Social Interactions

M. Pontil, Mirco Musolesi, C. Mascolo, 2009 .

Learning To Learn Around A Common Mean

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

Prospective motor control obeys to idiosyncratic strategies in autism

M. Pontil, C. Becchio, A. Cavallo, 2018, Scientific Reports.

From Margins to Probabilities in Multiclass Learning Problems

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

On Tuning Hyper-Parameters of Multiclass Margin Classifiers

Paolo Frasconi, Andrea Passerini, Massimiliano Pontil, 2002 .

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.

Entropy conditions for Lr-convergence of empirical processes

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

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.

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning

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

Image representations for object detection using kernel classifiers

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

Incremental Learning-to-Learn with Statistical Guarantees pdf

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

Sharp Spectral Rates for Koopman Operator Learning

M. Pontil, Karim Lounici, P. Novelli, 2023, 2302.02004.

Estimating Koopman operators with sketching to provably learn large scale dynamical systems

M. Pontil, L. Rosasco, P. Novelli, 2023, ArXiv.

Marthe: Scheduling the Learning Rate Via Online Hypergradients

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

Online Model Selection: a Rested Bandit Formulation pdf

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

Best Model Identification: A Rested Bandit Formulation

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

Meta Representation Learning with Contextual Linear Bandits

M. Pontil, Karim Lounici, Leonardo Cella, 2022, ArXiv.

Meta-learning with Stochastic Linear Bandits pdf

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

Multi-task and meta-learning with sparse linear bandits

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

New Perspectives on k-Support and Cluster Norms pdf

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

Combining Graph Laplacians for Semi-Supervised Learning

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

A note on different covering numbers in learning theory

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

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).

Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm

A. Tsybakov, M. Pontil, Evgenii Chzhen, 2023, 2306.02159.

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).

Regularizers for structured sparsity

Charles A. Micchelli, Massimiliano Pontil, Jean Morales, 2010, Advances in Computational Mathematics.

Reexamining Low Rank Matrix Factorization for Trace Norm Regularization pdf

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

MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES

T. Poggio, M. Pontil, B. Heisele, 2001 .

MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES

M. Pontil, B. Heisele, 2000 .

A Gang of Adversarial Bandits

M. Pontil, M. Herbster, Stephen Pasteris, 2021, NeurIPS.

An Algorithm for Transfer Learning in a Heterogeneous Environment

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

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

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

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

MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES

A. Verri, M. Pontil, Massimiliano Pontil, 2001 .

Fingerprint Classification with Combinations of Support Vector Machines

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

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).

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.

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 Journal on Advances in Signal Processing.

Recognizing 3-D Objects with Linear Support Vector Machines

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

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).

Prospective motor control obeys to idiosyncratic strategies in autism

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

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

P. Frasconi, M. Pontil, Luca Franceschi, 2019, ArXiv.

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

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

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.

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

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

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

Paolo Frasconi, Massimiliano Pontil, Fabrizio Costa, 2003 .

Learning Fair and Transferable Representations

M. Pontil, Andreas Maurer, Michele Donini, 2019, ArXiv.

Consistent Long-Term Forecasting of Ergodic Dynamical Systems

M. Pontil, Karim Lounici, P. Novelli, 2023, 2312.13426.

Learning invariant representations of time-homogeneous stochastic dynamical systems

M. Pontil, Karim Lounici, Riccardo Grazzi, 2023, 2307.09912.

Learning with optimal interpolation norms

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

Universal Multi-Task Kernels

C. Micchelli, M. Pontil, A. Caponnetto, 2008, J. Mach. Learn. Res..

Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs

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

Action Observation Areas Represent Intentions From Subtle Kinematic Features

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

On Learning Vector − Valued Functions

C. Micchelli, M. Pontil, 2003 .

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).

Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start

M. Pontil, Saverio Salzo, Riccardo Grazzi, 2022, J. Mach. Learn. Res..

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).

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).

Multi-task Representation Learning with Stochastic Linear Bandits

M. Pontil, Karim Lounici, Leonardo Cella, 2022, AISTATS.

Support vector machines vs multi-layer perceptrons in particle identification

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

Marthe: Scheduling the Learning Rate Via Online Hypergradients

P. Frasconi, M. Pontil, Luca Franceschi, 2019, International Joint Conference on Artificial Intelligence.