Direct Aspect-Based 3D Object Recognition. - Massimiliano Pontil; Alessandro Verri (ICIAP (2), 1997)

Recognizing 3-D Objects with Linear Support Vector Machines. - Massimiliano Pontil; Stefano Rogai; Alessandro Verri (ECCV (2), 1998)

Properties of Support Vector Machines. - Massimiliano Pontil; Alessandro Verri (Neural Comput., 1998)

Support Vector Machines for 3D Object Recognition. - Massimiliano Pontil; Alessandro Verri (IEEE Trans. Pattern Anal. Mach. Intell., 1998)

On the Vgamma Dimension for Regression in Reproducing Kernel Hilbert Spaces. - Theodoros Evgeniou; Massimiliano Pontil (ALT, 1999)

A Note on Support Vector Machine Degeneracy. - Ryan M. Rifkin; Massimiliano Pontil; Alessandro Verri (ALT, 1999)

Support vector machines vs multi-layer perceptrons in particle identification. - N. Barabino; M. Pallavicini; Alessandro Petrolini; Massimiliano Pontil; Alessandro Verri (ESANN, 1999)

From regression to classification in support vector machines. - Massimiliano Pontil; Ryan M. Rifkin; Theodoros Evgeniou (ESANN, 1999)

A Note on the Generalization Performance of Kernel Classifiers with Margin. - Theodoros Evgeniou; Massimiliano Pontil (ALT, 2000)

On the Noise Model of Support Vector Machines Regression. - Massimiliano Pontil; Sayan Mukherjee 004; Federico Girosi (ALT, 2000)

Bounds on the Generalization Performance of Kernel Machine Ensembles. - Theodoros Evgeniou; Luis Pérez-Breva; Massimiliano Pontil; Tomaso A. Poggio (ICML, 2000)

Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation. - Chikahito Nakajima; Norihiko Itoh; Massimiliano Pontil; Tomaso A. Poggio (ICPR, 2000)

People Recognition and Pose Estimation in Image Sequences. - Chikahito Nakajima; Massimiliano Pontil; Tomaso A. Poggio (IJCNN (4), 2000)

Feature Selection for SVMs. - Jason Weston; Sayan Mukherjee 001; Olivier Chapelle; Massimiliano Pontil; Tomaso A. Poggio; Vladimir Vapnik (NIPS, 2000)

Regularization Networks and Support Vector Machines. - Theodoros Evgeniou; Massimiliano Pontil; Tomaso A. Poggio (Adv. Comput. Math., 2000)

Statistical Learning Theory: A Primer. - Theodoros Evgeniou; Massimiliano Pontil; Tomaso A. Poggio (Int. J. Comput. Vis., 2000)

Support Vector Machines: Theory and Applications. - Theodoros Evgeniou; Massimiliano Pontil (Machine Learning and Its Applications, 2001)

A New Machine Learning Approach to Fingerprint Classification. - Yuan Yao; Gian Luca Marcialis; Massimiliano Pontil; Paolo Frasconi; Fabio Roli (AI*IA, 2001)

Fingerprint Classification with Combinations of Support Vector Machines. - Yuan Yao; Paolo Frasconi; Massimiliano Pontil (AVBPA, 2001)

Component-based Face Detection. - Bernd Heisele; Thomas Serre; Massimiliano Pontil; Tomaso A. Poggio (CVPR (1), 2001)

Categorization by Learning and Combining Object Parts. - Bernd Heisele; Thomas Serre; Massimiliano Pontil; Thomas Vetter; Tomaso A. Poggio (NIPS, 2001)

A Simple Algorithm for Learning Stable Machines. - Savina Andonova; André Elisseeff; Theodoros Evgeniou; Massimiliano Pontil (ECAI, 2002)

From Margins to Probabilities in Multiclass Learning Problems. - Andrea Passerini; Massimiliano Pontil; Paolo Frasconi (ECAI, 2002)

Support Vector Machines with Clustering for Training with Very Large Datasets. - Theodoros Evgeniou; Massimiliano Pontil (SETN, 2002)

Maintenance Training of Electric Power Facilities Using Object Recognition by SVM. - Chikahito Nakajima; Massimiliano Pontil (SVM, 2002)

Learning Preference Relations from Data. - Theodoros Evgeniou; Massimiliano Pontil (WIRN, 2002)

A Short Review of Statistical Learning Theory. - Massimiliano Pontil (WIRN, 2002)

Reproducing kernels and regularization methods in machine learning. - Massimiliano Pontil (ESANN, 2003)

On different ensembles of kernel machines. - Michiko Yamana; Hiroyuki Nakahara; Massimiliano Pontil; Shun-ichi Amari (ESANN, 2003)

A note on different covering numbers in learning theory. - Massimiliano Pontil (J. Complex., 2003)

Full-body person recognition system. - Chikahito Nakajima; Massimiliano Pontil; Bernd Heisele; Tomaso A. Poggio (Pattern Recognit., 2003)

Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines. - Yuan Yao; Gian Luca Marcialis; Massimiliano Pontil; Paolo Frasconi; Fabio Roli (Pattern Recognit., 2003)

Image Representations and Feature Selection for Multimedia Database Search. - Theodoros Evgeniou; Massimiliano Pontil; Constantine Papageorgiou; Tomaso A. Poggio (IEEE Trans. Knowl. Data Eng., 2003)

A Function Representation for Learning in Banach Spaces. - Charles A. Micchelli; Massimiliano Pontil (COLT, 2004)

Regularized multi–task learning. - Theodoros Evgeniou; Massimiliano Pontil (KDD, 2004)

Kernels for Multi–task Learning. - Charles A. Micchelli; Massimiliano Pontil (NIPS, 2004)

Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers. - Theodoros Evgeniou; Massimiliano Pontil; André Elisseeff (Mach. Learn., 2004)

New results on error correcting output codes of kernel machines. - Andrea Passerini; Massimiliano Pontil; Paolo Frasconi (IEEE Trans. Neural Networks, 2004)

Learning Convex Combinations of Continuously Parameterized Basic Kernels. - Andreas Argyriou; Charles A. Micchelli; Massimiliano Pontil (COLT, 2005)

Online learning over graphs. - Mark Herbster; Massimiliano Pontil; Lisa Wainer (ICML, 2005)

Combining Graph Laplacians for Semi-Supervised Learning. - Andreas Argyriou; Mark Herbster; Massimiliano Pontil (NIPS, 2005)

Stability of Randomized Learning Algorithms. - André Elisseeff; Theodoros Evgeniou; Massimiliano Pontil (J. Mach. Learn. Res., 2005)

Learning Multiple Tasks with Kernel Methods. - Theodoros Evgeniou; Charles A. Micchelli; Massimiliano Pontil (J. Mach. Learn. Res., 2005)

Learning the Kernel Function via Regularization. - Charles A. Micchelli; Massimiliano Pontil (J. Mach. Learn. Res., 2005)

On Learning Vector-Valued Functions. - Charles A. Micchelli; Massimiliano Pontil (Neural Comput., 2005)

Wide coverage natural language processing using kernel methods and neural networks for structured data. - Sauro Menchetti; Fabrizio Costa; Paolo Frasconi; Massimiliano Pontil (Pattern Recognit. Lett., 2005)

A DC-programming algorithm for kernel selection. - Andreas Argyriou; Raphael Hauser; Charles A. Micchelli; Massimiliano Pontil (ICML, 2006)

Multi-Task Feature Learning. - Andreas Argyriou; Theodoros Evgeniou; Massimiliano Pontil (NIPS, 2006)

Prediction on a Graph with a Perceptron. - Mark Herbster; Massimiliano Pontil (NIPS, 2006)

A Spectral Regularization Framework for Multi-Task Structure Learning. - Andreas Argyriou; Charles A. Micchelli; Massimiliano Pontil; Yiming Ying (NIPS, 2007)

Feature space perspectives for learning the kernel. - Charles A. Micchelli; Massimiliano Pontil (Mach. Learn., 2007)

A Uniform Lower Error Bound for Half-Space Learning. - Andreas Maurer; Massimiliano Pontil (ALT, 2008)

Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces. - Andreas Maurer; Massimiliano Pontil (ALT, 2008)

Online Prediction on Large Diameter Graphs. - Mark Herbster; Guy Lever; Massimiliano Pontil (NIPS, 2008)

Fast Prediction on a Tree. - Mark Herbster; Massimiliano Pontil; Sergio Rojas Galeano (NIPS, 2008)

An Algorithm for Transfer Learning in a Heterogeneous Environment. - Andreas Argyriou; Andreas Maurer; Massimiliano Pontil (ECML/PKDD (1), 2008)

Online Gradient Descent Learning Algorithms. - Yiming Ying; Massimiliano Pontil (Found. Comput. Math., 2008)

Universal Multi-Task Kernels. - Andrea Caponnetto; Charles A. Micchelli; Massimiliano Pontil; Yiming Ying (J. Mach. Learn. Res., 2008)

Convex multi-task feature learning. - Andreas Argyriou; Theodoros Evgeniou; Massimiliano Pontil (Mach. Learn., 2008)

Machine Learning Techniques for Biometrics. - Francesca Odone; Massimiliano Pontil; Alessandro Verri (Handbook of Remote Biometrics, 2009)

Taking Advantage of Sparsity in Multi-Task Learning. - Karim Lounici; Massimiliano Pontil; Alexandre B. Tsybakov; Sara A. van de Geer (COLT, 2009)

Empirical Bernstein Bounds and Sample-Variance Penalization. - Andreas Maurer; Massimiliano Pontil (COLT, 2009)

Entropy conditions for L r -convergence of empirical processes. - Andrea Caponnetto; Ernesto De Vito; Massimiliano Pontil (Adv. Comput. Math., 2009)

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. - Stefano Lise; Cédric Archambeau; Massimiliano Pontil; David T. Jones (BMC Bioinform., 2009)

When Is There a Representer Theorem? Vector Versus Matrix Regularizers. - Andreas Argyriou; Charles A. Micchelli; Massimiliano Pontil (J. Mach. Learn. Res., 2009)

A Family of Penalty Functions for Structured Sparsity. - Charles A. Micchelli; Jean Morales; Massimiliano Pontil (NIPS, 2010)

On Spectral Learning. - Andreas Argyriou; Charles A. Micchelli; Massimiliano Pontil (J. Mach. Learn. Res., 2010)

K -Dimensional Coding Schemes in Hilbert Spaces. - Augusto Maurer; Massimiliano Pontil (IEEE Trans. Inf. Theory, 2010)

An Empirical Study of Geographic User Activity Patterns in Foursquare. - Anastasios Noulas; Salvatore Scellato; Cecilia Mascolo; Massimiliano Pontil (ICWSM, 2011)

Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks. - Anastasios Noulas; Salvatore Scellato; Cecilia Mascolo; Massimiliano Pontil (The Social Mobile Web, 2011)

Efficient First Order Methods for Linear Composite Regularizers - Andreas Argyriou; Charles A. Micchelli; Massimiliano Pontil; Lixin Shen; Yuesheng Xu (CoRR, 2011)

A tale of many cities: universal patterns in human urban mobility - Anastasios Noulas; Salvatore Scellato; Renaud Lambiotte; Massimiliano Pontil; Cecilia Mascolo (CoRR, 2011)

Transfer learning in a heterogeneous environment. - Andreas Maurer; Massimiliano Pontil (CIP, 2012)

Structured sparsity regularization approach to the EEG inverse problem. - Jair Montoya-Martínez; Antonio Artés-Rodríguez; Lars Kai Hansen; Massimiliano Pontil (CIP, 2012)

Modelling transition dynamics in MDPs with RKHS embeddings. - Steffen Grünewälder; Guy Lever; Luca Baldassarre; Massimiliano Pontil; Arthur Gretton (ICML, 2012)

Conditional mean embeddings as regressors. - Steffen Grünewälder; Guy Lever; Arthur Gretton; Luca Baldassarre; Sam Patterson; Massimiliano Pontil (ICML, 2012)

Optimal kernel choice for large-scale two-sample tests. - Arthur Gretton; Bharath K. Sriperumbudur; Dino Sejdinovic; Heiko Strathmann; Sivaraman Balakrishnan; Massimiliano Pontil; Kenji Fukumizu (NIPS, 2012)

Structured Sparsity Models for Brain Decoding from fMRI Data. - Luca Baldassarre; Janaina Mourão Miranda; Massimiliano Pontil (PRNI, 2012)

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. - David T. Jones; Daniel W. A. Buchan; Domenico Cozzetto; Massimiliano Pontil (Bioinform., 2012)

Conditional mean embeddings as regressors - supplementary - Steffen Grünewälder; Guy Lever; Luca Baldassarre; Sam Patterson; Arthur Gretton; Massimiliano Pontil (CoRR, 2012)

A General Framework for Structured Sparsity via Proximal Optimization. - Luca Baldassarre; Jean Morales; Andreas Argyriou; Massimiliano Pontil (AISTATS, 2012)

Structured Sparsity and Generalization. - Andreas Maurer; Massimiliano Pontil (J. Mach. Learn. Res., 2012)

Exploiting Unrelated Tasks in Multi-Task Learning. - Bernardino Romera-Paredes; Andreas Argyriou; Nadia Berthouze; Massimiliano Pontil (AISTATS, 2012)

On Sparsity Inducing Regularization Methods for Machine Learning. - Andreas Argyriou; Luca Baldassarre; Charles A. Micchelli; Massimiliano Pontil (Empirical Inference, 2013)

Excess risk bounds for multitask learning with trace norm regularization. - Massimiliano Pontil; Andreas Maurer (COLT, 2013)

Transfer learning to account for idiosyncrasy in face and body expressions. - Bernardino Romera-Paredes; Min S. H. Aung; Massimiliano Pontil; Nadia Bianchi-Berthouze; Amanda C. de C. Williams; Paul J. Watson (FG, 2013)

Sparse coding for multitask and transfer learning. - Andreas Maurer; Massimiliano Pontil; Bernardino Romera-Paredes (ICML (2), 2013)

Multilinear Multitask Learning. - Bernardino Romera-Paredes; Hane Aung; Nadia Bianchi-Berthouze; Massimiliano Pontil (ICML (3), 2013)

A New Convex Relaxation for Tensor Completion. - Bernardino Romera-Paredes; Massimiliano Pontil (NIPS, 2013)

Multi-task Averaging via Task Clustering. - David Martínez-Rego; Massimiliano Pontil (SIMBAD, 2013)

Regularizers for structured sparsity. - Charles A. Micchelli; Jean Morales; Massimiliano Pontil (Adv. Comput. Math., 2013)

Structured sparse-low rank matrix factorization for the EEG inverse problem. - Jair Montoya-Martínez; Antonio Artés-Rodríguez; Massimiliano Pontil (CIP, 2014)

An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning. - Andreas Maurer; Massimiliano Pontil; Bernardino Romera-Paredes (COLT, 2014)

Large Margin Local Metric Learning. - Julien Bohné; Yiming Ying; Stéphane Gentric; Massimiliano Pontil (ECCV (2), 2014)

Spectral k-Support Norm Regularization. - Andrew M. McDonald; Massimiliano Pontil; Dimitris Stamos (NIPS, 2014)

A regularized matrix factorization approach to induce structured sparse-low-rank solutions in the EEG inverse problem. - Jair Montoya-Martínez; Antonio Artés-Rodríguez; Massimiliano Pontil; Lars Kai Hansen (EURASIP J. Adv. Signal Process., 2014)

Learning with dataset bias in latent subcategory models. - Dimitris Stamos; Samuele Martelli; Moin Nabi; Andrew M. McDonald; Vittorio Murino; Massimiliano Pontil (CVPR, 2015)

Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). - Trevor Darrell; Marius Kloft; Massimiliano Pontil; Gunnar Rätsch; Erik Rodner (Dagstuhl Reports, 2015)

Predicting a switching sequence of graph labelings. - Mark Herbster; Stephen Pasteris; Massimiliano Pontil (J. Mach. Learn. Res., 2015)

Fitting Spectral Decay with the k-Support Norm. - Andrew M. McDonald; Massimiliano Pontil; Dimitris Stamos (AISTATS, 2016)

Unsupervised Cross-Dataset Transfer Learning for Person Re-identification. - Peixi Peng; Tao Xiang 002; Yaowei Wang 001; Massimiliano Pontil; Shaogang Gong; Tiejun Huang 001; Yonghong Tian 001 (CVPR, 2016)

Similarity Function Learning with Data Uncertainty. - Julien Bohné; Sylvain Colin; Stéphane Gentric; Massimiliano Pontil (ICPRAM, 2016)

Distributed variance regularized Multitask Learning. - Michele Donini; David Martínez-Rego; Martin Goodson; John Shawe-Taylor; Massimiliano Pontil (IJCNN, 2016)

A multimodal multiple kernel learning approach to Alzheimer's disease detection. - Michele Donini; João M. Monteiro; Massimiliano Pontil; John Shawe-Taylor; Janaina Mourão Miranda (MLSP, 2016)

Mistake Bounds for Binary Matrix Completion. - Mark Herbster; Stephen Pasteris; Massimiliano Pontil (NIPS, 2016)

Bounds for Vector-Valued Function Estimation. - Andreas Maurer; Massimiliano Pontil (CoRR, 2016)

The Benefit of Multitask Representation Learning. - Andreas Maurer; Massimiliano Pontil; Bernardino Romera-Paredes (J. Mach. Learn. Res., 2016)

New Perspectives on k-Support and Cluster Norms. - Andrew M. McDonald; Massimiliano Pontil; Dimitris Stamos (J. Mach. Learn. Res., 2016)

Regret Bounds for Lifelong Learning. - Pierre Alquier; The Tien Mai; Massimiliano Pontil (AISTATS, 2017)

On Hyperparameter Optimization in Learning Systems. - Luca Franceschi 001; Michele Donini; Paolo Frasconi; Massimiliano Pontil (ICLR (Workshop), 2017)

Forward and Reverse Gradient-Based Hyperparameter Optimization. - Luca Franceschi 001; Michele Donini; Paolo Frasconi; Massimiliano Pontil (ICML, 2017)

A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion. - Leonardo Badino; Luca Franceschi 001; Raman Arora; Michele Donini; Massimiliano Pontil (INTERSPEECH, 2017)

Consistent Multitask Learning with Nonlinear Output Relations. - Carlo Ciliberto; Alessandro Rudi; Lorenzo Rosasco; Massimiliano Pontil (NIPS, 2017)

Quantum machine learning: a classical perspective. - Carlo Ciliberto; Mark Herbster; Alessandro Davide Ialongo; Massimiliano Pontil; Andrea Rocchetto; Simone Severini; Leonard Wossnig (CoRR, 2017)

Reexamining Low Rank Matrix Factorization for Trace Norm Regularization. - Carlo Ciliberto; Dimitris Stamos; Massimiliano Pontil (CoRR, 2017)

A Bridge Between Hyperparameter Optimization and Larning-to-learn. - Luca Franceschi 001; Michele Donini; Paolo Frasconi; Massimiliano Pontil (CoRR, 2017)

Empirical bounds for functions with weak interactions. - Andreas Maurer; Massimiliano Pontil (COLT, 2018)

Bilevel Programming for Hyperparameter Optimization and Meta-Learning. - Luca Franceschi 001; Paolo Frasconi; Saverio Salzo; Riccardo Grazzi; Massimiliano Pontil (ICML, 2018)

Learning To Learn Around A Common Mean. - Giulia Denevi; Carlo Ciliberto; Dimitris Stamos; Massimiliano Pontil (NeurIPS, 2018)

Empirical Risk Minimization Under Fairness Constraints. - Michele Donini; Luca Oneto; Shai Ben-David; John Shawe-Taylor; Massimiliano Pontil (NeurIPS, 2018)

Bilevel learning of the Group Lasso structure. - Jordan Frécon; Saverio Salzo; Massimiliano Pontil (NeurIPS, 2018)

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. - Giulia Luise; Alessandro Rudi; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2018)

Incremental Learning-to-Learn with Statistical Guarantees. - Giulia Denevi; Carlo Ciliberto; Dimitris Stamos; Massimiliano Pontil (UAI, 2018)

Bilevel Programming for Hyperparameter Optimization and Meta-Learning. - Luca Franceschi 001; Paolo Frasconi; Saverio Salzo; Massimiliano Pontil (CoRR, 2018)

Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning. - Luca Franceschi 001; Riccardo Grazzi; Massimiliano Pontil; Saverio Salzo; Paolo Frasconi (CoRR, 2018)

Fast and Continuous Foothold Adaptation for Dynamic Locomotion through Convolutional Neural Networks. - Octavio Antonio Villarreal-Magaña; Victor Barasuol; Marco Camurri; Michele Focchi; Luca Franceschi 001; Massimiliano Pontil; Darwin G. Caldwell; Claudio Semini (CoRR, 2018)

Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning. - Peixi Peng; Yonghong Tian 001; Tao Xiang 002; Yaowei Wang 001; Massimiliano Pontil; Tiejun Huang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

Learning local metrics from pairwise similarity data. - Julien Bohné; Yiming Ying; Stéphane Gentric; Massimiliano Pontil (Pattern Recognit., 2018)

Taking Advantage of Multitask Learning for Fair Classification. - Luca Oneto; Michele Donini; Amon Elders; Massimiliano Pontil (AIES, 2019)

Uniform concentration and symmetrization for weak interactions. - Andreas Maurer; Massimiliano Pontil (COLT, 2019)

PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. - Luca Oneto; Michele Donini; Massimiliano Pontil (ESANN, 2019)

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization. - Giulia Denevi; Carlo Ciliberto; Riccardo Grazzi; Massimiliano Pontil (ICML, 2019)

Learning Discrete Structures for Graph Neural Networks. - Luca Franceschi 001; Mathias Niepert; Massimiliano Pontil; Xiao He (ICML, 2019)

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. - Giulia Luise; Dimitrios Stamos; Massimiliano Pontil; Carlo Ciliberto (ICML, 2019)

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification. - Evgenii Chzhen; Christophe Denis; Mohamed Hebiri; Luca Oneto; Massimiliano Pontil (NeurIPS, 2019)

Online-Within-Online Meta-Learning. - Giulia Denevi; Dimitris Stamos; Carlo Ciliberto; Massimiliano Pontil (NeurIPS, 2019)

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. - Giulia Luise; Saverio Salzo; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2019)

Learning Fair and Transferable Representations. - Luca Oneto; Michele Donini; Andreas Maurer; Massimiliano Pontil (CoRR, 2019)

Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm. - Michele Donini; Luca Franceschi 001; Massimiliano Pontil; Orchid Majumder; Paolo Frasconi (CoRR, 2019)

Learning with optimal interpolation norms. - Patrick L. Combettes; Andrew M. McDonald; Charles A. Micchelli; Massimiliano Pontil (Numer. Algorithms, 2019)

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. - Michele Donini; João M. Monteiro; Massimiliano Pontil; Tim Hahn; Andreas J. Fallgatter; John Shawe-Taylor; Janaina Mourão Miranda (NeuroImage, 2019)

Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs. - Octavio Antonio Villarreal-Magaña; Victor Barasuol; Marco Camurri; Luca Franceschi 001; Michele Focchi; Massimiliano Pontil; Darwin G. Caldwell; Claudio Semini (IEEE Robotics Autom. Lett., 2019)

Learning Fair and Transferable Representations with Theoretical Guarantees. - Luca Oneto; Michele Donini; Massimiliano Pontil; Andreas Maurer (DSAA, 2020)

Meta-learning with Stochastic Linear Bandits. - Leonardo Cella; Alessandro Lazaric; Massimiliano Pontil (ICML, 2020)

On the Iteration Complexity of Hypergradient Computation. - Riccardo Grazzi; Luca Franceschi 001; Massimiliano Pontil; Saverio Salzo (ICML, 2020)

Unveiling Groups of Related Tasks in Multi - Task Learning. - Jordan Frécon; Saverio Salzo; Massimiliano Pontil (ICPR, 2020)

Marthe: Scheduling the Learning Rate Via Online Hypergradients. - Michele Donini; Luca Franceschi 001; Orchid Majumder; Massimiliano Pontil; Paolo Frasconi (IJCAI, 2020)

General Fair Empirical Risk Minimization. - Luca Oneto; Michele Donini; Massimiliano Pontil (IJCNN, 2020)

Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits. - Arya Akhavan; Massimiliano Pontil; Alexandre B. Tsybakov (NeurIPS, 2020)

Fair regression with Wasserstein barycenters. - Evgenii Chzhen; Christophe Denis; Mohamed Hebiri; Luca Oneto; Massimiliano Pontil (NeurIPS, 2020)

Fair regression via plug-in estimator and recalibration with statistical guarantees. - Evgenii Chzhen; Christophe Denis; Mohamed Hebiri; Luca Oneto; Massimiliano Pontil (NeurIPS, 2020)

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning. - Giulia Denevi; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2020)

Estimating weighted areas under the ROC curve. - Andreas Maurer; Massimiliano Pontil (NeurIPS, 2020)

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. - Luca Oneto; Michele Donini; Giulia Luise; Carlo Ciliberto; Andreas Maurer; Massimiliano Pontil (NeurIPS, 2020)

Online Parameter-Free Learning of Multiple Low Variance Tasks. - Giulia Denevi; Massimiliano Pontil; Dimitrios Stamos (UAI, 2020)

Efficient Tensor Kernel methods for sparse regression. - Feliks Hibraj; Marcello Pelillo; Saverio Salzo; Massimiliano Pontil (CoRR, 2020)

Generalization Properties of Optimal Transport GANs with Latent Distribution Learning. - Giulia Luise; Massimiliano Pontil; Carlo Ciliberto (CoRR, 2020)

Online Model Selection: a Rested Bandit Formulation. - Leonardo Cella; Claudio Gentile; Massimiliano Pontil (CoRR, 2020)

A Perturbation Resilient Framework for Unsupervised Learning. - Andreas Maurer; Daniela A. Parletta; Andrea Paudice; Massimiliano Pontil (CoRR, 2020)

Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy. - Luca Oneto; Michele Donini; Massimiliano Pontil; John Shawe-Taylor (Neurocomputing, 2020)

Machine Learning for Cultural Heritage: A Survey. - Marco Fiorucci; Marina Khoroshiltseva; Massimiliano Pontil; Arianna Traviglia; Alessio Del Bue; Stuart James (Pattern Recognit. Lett., 2020)

Approximating Hamiltonian dynamics with the Nyström method. - Alessandro Rudi; Leonard Wossnig; Carlo Ciliberto; Andrea Rocchetto; Massimiliano Pontil; Simone Severini (Quantum, 2020)

Convergence Properties of Stochastic Hypergradients. - Riccardo Grazzi; Massimiliano Pontil; Saverio Salzo (AISTATS, 2021)

Distance-Based Regularisation of Deep Networks for Fine-Tuning. - Henry Gouk; Timothy M. Hospedales; Massimiliano Pontil (ICLR, 2021)

Best Model Identification: A Rested Bandit Formulation. - Leonardo Cella; Massimiliano Pontil; Claudio Gentile (ICML, 2021)

Robust Unsupervised Learning via L-statistic Minimization. - Andreas Maurer; Daniela Angela Parletta; Andrea Paudice; Massimiliano Pontil (ICML, 2021)

Distributed Zero-Order Optimization under Adversarial Noise. - Arya Akhavan; Massimiliano Pontil; Alexandre B. Tsybakov (NeurIPS, 2021)

A Gang of Adversarial Bandits. - Mark Herbster; Stephen Pasteris; Fabio Vitale; Massimiliano Pontil (NeurIPS, 2021)

Concentration inequalities under sub-Gaussian and sub-exponential conditions. - Andreas Maurer; Massimiliano Pontil (NeurIPS, 2021)

The Role of Global Labels in Few-Shot Classification and How to Infer Them. - Ruohan Wang; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2021)

Multi-task and meta-learning with sparse linear bandits. - Leonardo Cella; Massimiliano Pontil (UAI, 2021)

Some Hoeffding- and Bernstein-type Concentration Inequalities. - Andreas Maurer; Massimiliano Pontil (CoRR, 2021)

Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport. - Vladimir Kostic; Saverio Salzo; Massimiliano Pontil (CoRR, 2021)

Bregman Neural Networks. - Jordan Frécon; Gilles Gasso; Massimiliano Pontil; Saverio Salzo (ICML, 2022)

Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity. - Vladimir R. Kostic; Saverio Salzo; Massimiliano Pontil (ICML, 2022)

Distribution Regression with Sliced Wasserstein Kernels. - Dimitri Meunier; Massimiliano Pontil; Carlo Ciliberto (ICML, 2022)

A gradient estimator via L1-randomization for online zero-order optimization with two point feedback. - Arya Akhavan; Evgenii Chzhen; Massimiliano Pontil; Alexandre B. Tsybakov (NeurIPS, 2022)

Conditional Meta-Learning of Linear Representations. - Giulia Denevi; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2022)

Group Meritocratic Fairness in Linear Contextual Bandits. - Riccardo Grazzi; Arya Akhavan; John Isak Texas Falk; Leonardo Cella; Massimiliano Pontil (NeurIPS, 2022)

Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. - Vladimir Kostic; Pietro Novelli; Andreas Maurer; Carlo Ciliberto; Lorenzo Rosasco; Massimiliano Pontil (NeurIPS, 2022)

Implicit kernel meta-learning using kernel integral forms. - John Isak Texas Falk; Carlo Ciliberto; Massimiliano Pontil (UAI, 2022)

Multi-source domain adaptation via weighted joint distributions optimal transport. - Rosanna Turrisi; Rémi Flamary; Alain Rakotomamonjy; Massimiliano Pontil (UAI, 2022)

Multi-task Representation Learning with Stochastic Linear Bandits. - Leonardo Cella; Karim Lounici; Massimiliano Pontil (CoRR, 2022)

Characterizing metastable states with the help of machine learning. - Pietro Novelli; Luigi Bonati; Massimiliano Pontil; Michele Parrinello (CoRR, 2022)

Meta Representation Learning with Contextual Linear Bandits. - Leonardo Cella; Karim Lounici; Massimiliano Pontil (CoRR, 2022)

Schedule-Robust Online Continual Learning. - Ruohan Wang; Marco Ciccone; Giulia Luise; Andrew Yapp; Massimiliano Pontil; Carlo Ciliberto (CoRR, 2022)

Multiple Instance Learning for Emotion Recognition Using Physiological Signals. - Luca Romeo; Andrea Cavallo; Lucia Pepa; Nadia Bianchi-Berthouze; Massimiliano Pontil (IEEE Trans. Affect. Comput., 2022)

Multitask Online Mirror Descent. - Nicolò Cesa-Bianchi; Pierre Laforgue; Andrea Paudice; Massimiliano Pontil (Trans. Mach. Learn. Res., 2022)

Multi-task Representation Learning with Stochastic Linear Bandits. - Leonardo Cella; Karim Lounici; Grégoire Pacreau; Massimiliano Pontil (AISTATS, 2023)

Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. - John Isak Texas Falk; Luigi Bonati; Pietro Novelli; Michele Parrinello; Massimiliano Pontil (NeurIPS, 2023)

Sharp Spectral Rates for Koopman Operator Learning. - Vladimir Kostic; Karim Lounici; Pietro Novelli; Massimiliano Pontil (NeurIPS, 2023)

Estimating Koopman operators with sketching to provably learn large scale dynamical systems. - Giacomo Meanti; Antoine Chatalic; Vladimir Kostic; Pietro Novelli; Massimiliano Pontil; Lorenzo Rosasco (NeurIPS, 2023)

Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues. - Vladimir Kostic; Karim Lounici; Pietro Novelli; Massimiliano Pontil (CoRR, 2023)

Deep projection networks for learning time-homogeneous dynamical systems. - Vladimir R. Kostic; Pietro Novelli; Riccardo Grazzi; Karim Lounici; Massimiliano Pontil (CoRR, 2023)

A randomized algorithm to solve reduced rank operator regression. - Giacomo Turri; Vladimir Kostic; Pietro Novelli; Massimiliano Pontil (CoRR, 2023)

Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. - Riccardo Grazzi; Massimiliano Pontil; Saverio Salzo (J. Mach. Learn. Res., 2023)

Learning invariant representations of time-homogeneous stochastic dynamical systems. - Vladimir R. Kostic; Pietro Novelli; Riccardo Grazzi; Karim Lounici; Massimiliano Pontil (ICLR, 2024)

Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates. - Riccardo Grazzi; Massimiliano Pontil; Saverio Salzo (ICML, 2024)

Consistent Long-Term Forecasting of Ergodic Dynamical Systems. - Vladimir R. Kostic; Karim Lounici; Prune Inzerilli; Pietro Novelli; Massimiliano Pontil (ICML, 2024)

Leveraging Symmetry in RL-based Legged Locomotion Control. - Zhi Su; Xiaoyu Huang; Daniel Felipe Ordoñez Apraez; Yunfei Li 005; Zhongyu Li 003; Qiayuan Liao; Giulio Turrisi; Massimiliano Pontil; Claudio Semini; Yi Wu 013; Koushil Sreenath (IROS, 2024)

Dynamics harmonic analysis of robotic systems: Application in data-driven Koopman modelling. - Daniel Felipe Ordoñez Apraez; Vladimir Kostic; Giulio Turrisi; Pietro Novelli; Carlos Mastalli; Claudio Semini; Massimiliano Pontil (L4DC, 2024)

From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach. - Timothée Devergne; Vladimir Kostic; Michele Parrinello; Massimiliano Pontil (NeurIPS, 2024)

Learning the Infinitesimal Generator of Stochastic Diffusion Processes. - Vladimir Kostic; Hélène Halconruy; Timothée Devergne; Karim Lounici; Massimiliano Pontil (NeurIPS, 2024)

Neural Conditional Probability for Uncertainty Quantification. - Vladimir Kostic; Grégoire Pacreau; Giacomo Turri; Pietro Novelli; Karim Lounici; Massimiliano Pontil (NeurIPS, 2024)

Operator World Models for Reinforcement Learning. - Pietro Novelli; Marco Pratticò; Massimiliano Pontil; Carlo Ciliberto (NeurIPS, 2024)

Morphological Symmetries in Robotics. - Daniel Felipe Ordoñez Apraez; Giulio Turrisi; Vladimir Kostic; Mario Martín; Antonio Agudo; Francesc Moreno-Noguer; Massimiliano Pontil; Claudio Semini; Carlos Mastalli (CoRR, 2024)

Contextual Continuum Bandits: Static Versus Dynamic Regret. - Arya Akhavan; Karim Lounici; Massimiliano Pontil; Alexandre B. Tsybakov (CoRR, 2024)

Neural Conditional Probability for Inference. - Vladimir R. Kostic; Karim Lounici; Grégoire Pacreau; Pietro Novelli; Giacomo Turri; Massimiliano Pontil (CoRR, 2024)

Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth. - Leonardo Sabattini; Annalisa Coriolano; Corneel Casert; Stiven Forti; Edward S. Barnard; Fabio Beltram; Massimiliano Pontil; Stephen Whitelam; Camilla Coletti; Antonio Rossi (CoRR, 2024)

Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups. - Vladimir R. Kostic; Karim Lounici; Hélène Halconruy; Timothee Devergne; Pietro Novelli; Massimiliano Pontil (CoRR, 2024)

Hyperparameter Optimization in Machine Learning. - Luca Franceschi 001; Michele Donini; Valerio Perrone; Aaron Klein; Cédric Archambeau; Matthias W. Seeger; Massimiliano Pontil; Paolo Frasconi (CoRR, 2024)

QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex. - Salvatore Calcagno 002; Isaak Kavasidis; Simone Palazzo; Marco Brondi; Luca Sità; Giacomo Turri; Daniela Giordano; Vladimir R. Kostic; Tommaso Fellin; Massimiliano Pontil; Concetto Spampinato (CoRR, 2024)

High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise]. - Daniela Angela Parletta; Andrea Paudice; Massimiliano Pontil; Saverio Salzo (SIAM J. Math. Data Sci., 2024)

Multi-Rater Consensus Learning for Modeling Multiple Sparse Ratings of Affective Behaviour. - Luca Romeo; Temitayo A. Olugbade; Massimiliano Pontil; Nadia Bianchi-Berthouze (IEEE Trans. Affect. Comput., 2024)

Robust Meta-Representation Learning via Global Label Inference and Classification. - Ruohan Wang; John Isak Texas Falk; Massimiliano Pontil; Carlo Ciliberto (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications. - Erfan Mirzaei; Andreas Maurer; Vladimir R. Kostic; Massimiliano Pontil (AISTATS, 2025)

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues. - Riccardo Grazzi; Julien Siems; Arber Zela; Jörg K. H. Franke; Frank Hutter; Massimiliano Pontil (ICLR, 2025)

DeltaProduct: Increasing the Expressivity of DeltaNet Through Products of Householders. - Julien Siems; Timur Carstensen; Arber Zela; Frank Hutter; Massimiliano Pontil; Riccardo Grazzi (CoRR, 2025)

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems. - Giacomo Turri; Luigi Bonati; Kai Zhu; Massimiliano Pontil; Pietro Novelli (CoRR, 2025)

Equivariant Representation Learning for Symmetry-Aware Inference with Guarantees. - Daniel Felipe Ordoñez Apraez; Vladimir Kostic; Alek Fröhlich; Vivien Brandt; Karim Lounici; Massimiliano Pontil (CoRR, 2025)