Pontil, Massimiliano

123 publications

ICML 2025 A Bregman Proximal Viewpoint on Neural Operators Abdel-Rahim Mezidi, Jordan Patracone, Saverio Salzo, Amaury Habrard, Massimiliano Pontil, Rémi Emonet, Marc Sebban

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

NeurIPS 2025 DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products Julien Siems, Timur Carstensen, Arber Zela, Frank Hutter, Massimiliano Pontil, Riccardo Grazzi

ICLRW 2025 DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products Julien Siems, Timur Carstensen, Arber Zela, Frank Hutter, Massimiliano Pontil, Riccardo Grazzi

FnTML 2025 Hyperparameter Optimization in Machine Learning Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi

ICML 2025 Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups Vladimir R Kostic, Karim Lounici, Hélène Halconruy, Timothée Devergne, Pietro Novelli, Massimiliano Pontil

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

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

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

JMLR 2024 Gradient-Free Optimization of Highly Smooth Functions: Improved Analysis and a New Algorithm Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov

ICLR 2024 Learning Invariant Representations of Time-Homogeneous Stochastic Dynamical Systems Vladimir R Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil

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

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

ICML 2024 Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

NeurIPS 2024 Operator World Models for Reinforcement Learning Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo Ciliberto

NeurIPSW 2024 Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues Riccardo Grazzi, Julien Siems, Jörg K.H. Franke, Arber Zela, Frank Hutter, Massimiliano Pontil

JMLR 2023 Bilevel Optimization with a Lower-Level Contraction: Optimal Sample Complexity Without Warm-Start Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

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

AISTATS 2023 Multi-Task Representation Learning with Stochastic Linear Bandits Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil

NeurIPS 2023 Sharp Spectral Rates for Koopman Operator Learning Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil

NeurIPS 2023 Transfer Learning for Atomistic Simulations Using GNNs and Kernel Mean Embeddings John Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil

NeurIPS 2022 A Gradient Estimator via L1-Randomization for Online Zero-Order Optimization with Two Point Feedback Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre Tsybakov

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 Bregman Neural Networks Jordan Frecon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo

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

ICML 2022 Distribution Regression with Sliced Wasserstein Kernels Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto

NeurIPS 2022 Group Meritocratic Fairness in Linear Contextual Bandits Riccardo Grazzi, Arya Akhavan, John IF Falk, Leonardo Cella, Massimiliano Pontil

UAI 2022 Implicit Kernel Meta-Learning Using Kernel Integral Forms John Isak Texas Falk, Carlo Cilibert, 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

UAI 2022 Multi-Source Domain Adaptation via Weighted Joint Distributions Optimal Transport Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil

TMLR 2022 Multitask Online Mirror Descent Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil

AISTATS 2021 Convergence Properties of Stochastic Hypergradients Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

NeurIPS 2021 A Gang of Adversarial Bandits Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil

ICML 2021 Best Model Identification: A Rested Bandit Formulation Leonardo Cella, Massimiliano Pontil, Claudio Gentile

NeurIPS 2021 Concentration Inequalities Under Sub-Gaussian and Sub-Exponential Conditions Andreas Maurer, Massimiliano Pontil

ICLR 2021 Distance-Based Regularisation of Deep Networks for Fine-Tuning Henry Gouk, Timothy Hospedales, Massimiliano Pontil

NeurIPS 2021 Distributed Zero-Order Optimization Under Adversarial Noise Arya Akhavan, Massimiliano Pontil, Alexandre Tsybakov

UAI 2021 Multi-Task and Meta-Learning with Sparse Linear Bandits Leonardo Cella, Massimiliano Pontil

ICML 2021 Robust Unsupervised Learning via L-Statistic Minimization Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, 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 2020 Estimating Weighted Areas Under the ROC Curve Andreas Maurer, Massimiliano Pontil

NeurIPS 2020 Exploiting Higher Order Smoothness in Derivative-Free Optimization and Continuous Bandits Arya Akhavan, Massimiliano Pontil, Alexandre Tsybakov

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 Fair Regression via Plug-in Estimator and Recalibration with Statistical Guarantees Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil

NeurIPS 2020 Fair Regression with Wasserstein Barycenters Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil

IJCAI 2020 Marthe: Scheduling the Learning Rate via Online Hypergradients Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi

ICML 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, Massimiliano Pontil, Saverio Salzo

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

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

ICML 2019 Learning Discrete Structures for Graph Neural Networks Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He

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

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

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

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

COLT 2019 Uniform Concentration and Symmetrization for Weak Interactions Andreas Maurer, Massimiliano Pontil

NeurIPS 2018 Bilevel Learning of the Group Lasso Structure Jordan Frecon, Saverio Salzo, Massimiliano Pontil

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

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

COLT 2018 Empirical Bounds for Functions with Weak Interactions Andreas Maurer, Massimiliano Pontil

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

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

NeurIPS 2018 Learning to Learn Around a Common Mean Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil

NeurIPS 2017 Consistent Multitask Learning with Nonlinear Output Relations Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil

ICML 2017 Forward and Reverse Gradient-Based Hyperparameter Optimization Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil

ICLR 2017 On Hyperparameter Optimization in Learning Systems Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil

AISTATS 2017 Regret Bounds for Lifelong Learning Pierre Alquier, The Tien Mai, Massimiliano Pontil

AISTATS 2016 Fitting Spectral Decay with the K-Support Norm Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos

NeurIPS 2016 Mistake Bounds for Binary Matrix Completion Mark Herbster, Stephen Pasteris, Massimiliano Pontil

JMLR 2016 New Perspectives on K-Support and Cluster Norms Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos

JMLR 2016 The Benefit of Multitask Representation Learning Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes

CVPR 2016 Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification Peixi Peng, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian

CVPR 2015 Learning with Dataset Bias in Latent Subcategory Models Dimitris Stamos, Samuele Martelli, Moin Nabi, Andrew McDonald, Vittorio Murino, Massimiliano Pontil

JMLR 2015 Predicting a Switching Sequence of Graph Labelings Mark Herbster, Stephen Pasteris, Massimiliano Pontil

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

ECCV 2014 Large Margin Local Metric Learning Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil

NeurIPS 2014 Spectral K-Support Norm Regularization Andrew M McDonald, Massimiliano Pontil, Dimitris Stamos

NeurIPS 2013 A New Convex Relaxation for Tensor Completion Bernardino Romera-Paredes, Massimiliano Pontil

COLT 2013 Excess Risk Bounds for Multitask Learning with Trace Norm Regularization Massimiliano Pontil, Andreas Maurer

ICML 2013 Multilinear Multitask Learning Bernardino Romera-Paredes, Hane Aung, Nadia Bianchi-Berthouze, Massimiliano Pontil

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

ICML 2012 Conditional Mean Embeddings as Regressors Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil

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

ICML 2012 Modelling Transition Dynamics in MDPs with RKHS Embeddings Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton

NeurIPS 2012 Optimal Kernel Choice for Large-Scale Two-Sample Tests Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, Bharath K. Sriperumbudur

JMLR 2012 Structured Sparsity and Generalization Andreas Maurer, Massimiliano Pontil

NeurIPS 2010 A Family of Penalty Functions for Structured Sparsity Jean Morales, Charles A. Micchelli, Massimiliano Pontil

JMLR 2010 On Spectral Learning Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil

COLT 2009 Empirical Bernstein Bounds and Sample-Variance Penalization Andreas Maurer, Massimiliano Pontil

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

JMLR 2009 When Is There a Representer Theorem? Vector Versus Matrix Regularizers Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil

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

ECML-PKDD 2008 An Algorithm for Transfer Learning in a Heterogeneous Environment Andreas Argyriou, Andreas Maurer, Massimiliano Pontil

MLJ 2008 Convex Multi-Task Feature Learning Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil

NeurIPS 2008 Fast Prediction on a Tree Mark Herbster, Massimiliano Pontil, Sergio R. Galeano

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

NeurIPS 2008 Online Prediction on Large Diameter Graphs Mark Herbster, Guy Lever, Massimiliano Pontil

JMLR 2008 Universal Multi-Task Kernels Andrea Caponnetto, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying

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

MLJ 2007 Feature Space Perspectives for Learning the Kernel Charles A. Micchelli, Massimiliano Pontil

ICML 2006 A DC-Programming Algorithm for Kernel Selection Andreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil

NeurIPS 2006 Multi-Task Feature Learning Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil

NeurIPS 2006 Prediction on a Graph with a Perceptron Mark Herbster, Massimiliano Pontil

NeurIPS 2005 Combining Graph Laplacians for Semi--Supervised Learning Andreas Argyriou, Mark Herbster, Massimiliano Pontil

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

JMLR 2005 Learning Multiple Tasks with Kernel Methods Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil

JMLR 2005 Learning the Kernel Function via Regularization Charles A. Micchelli, Massimiliano Pontil

ICML 2005 Online Learning over Graphs Mark Herbster, Massimiliano Pontil, Lisa Wainer

JMLR 2005 Stability of Randomized Learning Algorithms Andre Elisseeff, Theodoros Evgeniou, Massimiliano Pontil

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

NeurIPS 2004 Kernels for Multi--Task Learning Charles A. Micchelli, Massimiliano Pontil

MLJ 2004 Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers Theodoros Evgeniou, Massimiliano Pontil, André Elisseeff

NeurIPS 2001 Categorization by Learning and Combining Object Parts Bernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso Poggio

CVPR 2001 Component-Based Face Detection Bernd Heisele, Thomas Serre, Massimiliano Pontil, Tomaso A. Poggio

ALT 2000 A Note on the Generalization Performance of Kernel Classifiers with Margin Theodoros Evgeniou, Massimiliano Pontil

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

NeurIPS 2000 Feature Selection for SVMs Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso Poggio, Vladimir Vapnik

ALT 2000 On the Noise Model of Support Vector Machines Regression Massimiliano Pontil, Sayan Mukherjee, Federico Girosi

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

ALT 1999 On the Vgamma Dimension for Regression in Reproducing Kernel Hilbert Spaces Theodoros Evgeniou, Massimiliano Pontil

NeCo 1998 Properties of Support Vector Machines Massimiliano Pontil, Alessandro Verri

ECCV 1998 Recognizing 3-D Objects with Linear Support Vector Machines Massimiliano Pontil, Stefano Rogai, Alessandro Verri