Massimiliano Pontil

ORCID

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 148

Top Venues

Publications

Zhi Su, Xiaoyu Huang, Daniel Felipe Ordoñez Apraez, Yunfei Li, Zhongyu Li, Qiayuan Liao, Giulio Turrisi, Massimiliano Pontil, Claudio Semini, Yi Wu, Koushil Sreenath
Leveraging Symmetry in RL-based Legged Locomotion Control. CoRR (2024)
Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil
Learning invariant representations of time-homogeneous stochastic dynamical systems. ICLR (2024)
Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo Ciliberto
Operator World Models for Reinforcement Learning. CoRR (2024)
Daniel Felipe Ordoñez Apraez, Giulio Turrisi, Vladimir Kostic, Mario Martín, Antonio Agudo, Francesc Moreno-Noguer, Massimiliano Pontil, Claudio Semini, Carlos Mastalli
Morphological Symmetries in Robotics. CoRR (2024)
Vladimir R. Kostic, Karim Lounici, Grégoire Pacreau, Pietro Novelli, Giacomo Turri, Massimiliano Pontil
Neural Conditional Probability for Inference. CoRR (2024)
Vladimir R. Kostic, Karim Lounici, Hélène Halconruy, Timothee Devergne, Massimiliano Pontil
Learning the Infinitesimal Generator of Stochastic Diffusion Processes. CoRR (2024)
Arya Akhavan, Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov
Contextual Continuum Bandits: Static Versus Dynamic Regret. CoRR (2024)
Timothee Devergne, Vladimir Kostic, Michele Parrinello, Massimiliano Pontil
From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach. CoRR (2024)
Ruohan Wang, John Isak Texas Falk, Massimiliano Pontil, Carlo Ciliberto
Robust Meta-Representation Learning via Global Label Inference and Classification. IEEE Trans. Pattern Anal. Mach. Intell. 46 (4) (2024)
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates. CoRR (2024)
Daniel Felipe Ordoñez Apraez, Vladimir Kostic, Giulio Turrisi, Pietro Novelli, Carlos Mastalli, Claudio Semini, Massimiliano Pontil
Dynamics harmonic analysis of robotic systems: Application in data-driven Koopman modelling. L4DC (2024)
Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco
Estimating Koopman operators with sketching to provably learn large scale dynamical systems. CoRR (2023)
Giacomo Turri, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil
A randomized algorithm to solve reduced rank operator regression. CoRR (2023)
John Isak Texas Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. NeurIPS (2023)
Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil
Multi-task Representation Learning with Stochastic Linear Bandits. AISTATS (2023)
Daniel Felipe Ordoñez Apraez, Vladimir Kostic, Giulio Turrisi, Pietro Novelli, Carlos Mastalli, Claudio Semini, Massimiliano Pontil
Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling. CoRR (2023)
Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil
Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues. CoRR (2023)
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. J. Mach. Learn. Res. 24 (2023)
Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil
Deep projection networks for learning time-homogeneous dynamical systems. CoRR (2023)
Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil
Sharp Spectral Rates for Koopman Operator Learning. NeurIPS (2023)
Giacomo Meanti, Antoine Chatalic, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco
Estimating Koopman operators with sketching to provably learn large scale dynamical systems. NeurIPS (2023)
Prune Inzerilli, Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil
Consistent Long-Term Forecasting of Ergodic Dynamical Systems. CoRR (2023)
John Isak Texas Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. CoRR (2023)
Jordan Frécon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo
Bregman Neural Networks. ICML (2022)
Luca Romeo, Andrea Cavallo, Lucia Pepa, Nadia Bianchi-Berthouze, Massimiliano Pontil
Multiple Instance Learning for Emotion Recognition Using Physiological Signals. IEEE Trans. Affect. Comput. 13 (1) (2022)
John Isak Texas Falk, Carlo Ciliberto, Massimiliano Pontil
Implicit kernel meta-learning using kernel integral forms. UAI (2022)
Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. CoRR (2022)
Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil
Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. NeurIPS (2022)
Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
Conditional Meta-Learning of Linear Representations. NeurIPS (2022)
Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil
Group Meritocratic Fairness in Linear Contextual Bandits. NeurIPS (2022)
Pietro Novelli, Luigi Bonati, Massimiliano Pontil, Michele Parrinello
Characterizing metastable states with the help of machine learning. CoRR (2022)
Leonardo Cella, Karim Lounici, Massimiliano Pontil
Meta Representation Learning with Contextual Linear Bandits. CoRR (2022)
Rosanna Turrisi, Réemi Flamary, Alain Rakotomamonjy, Massimiliano Pontil
Multi-source domain adaptation via weighted joint distributions optimal transport. UAI (2022)
Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil
Group Meritocratic Fairness in Linear Contextual Bandits. CoRR (2022)
Vladimir R. Kostic, Saverio Salzo, Massimiliano Pontil
Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity. ICML (2022)
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. CoRR (2022)
Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil
Multitask Online Mirror Descent. Trans. Mach. Learn. Res. 2022 (2022)
Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto
Distribution Regression with Sliced Wasserstein Kernels. ICML (2022)
Ruohan Wang, John Isak Texas Falk, Massimiliano Pontil, Carlo Ciliberto
Robust Meta-Representation Learning via Global Label Inference and Classification. CoRR (2022)
Ruohan Wang, Marco Ciccone, Giulia Luise, Andrew Yapp, Massimiliano Pontil, Carlo Ciliberto
Schedule-Robust Online Continual Learning. CoRR (2022)
Leonardo Cella, Karim Lounici, Massimiliano Pontil
Multi-task Representation Learning with Stochastic Linear Bandits. CoRR (2022)
Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback. NeurIPS (2022)
Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto
Distribution Regression with Sliced Wasserstein Kernels. CoRR (2022)
Leonardo Cella, Massimiliano Pontil, Claudio Gentile
Best Model Identification: A Rested Bandit Formulation. ICML (2021)
Andreas Maurer, Massimiliano Pontil
Concentration inequalities under sub-Gaussian and sub-exponential conditions. NeurIPS (2021)
Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto
The Role of Global Labels in Few-Shot Classification and How to Infer Them. CoRR (2021)
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
Convergence Properties of Stochastic Hypergradients. AISTATS (2021)
Vladimir Kostic, Saverio Salzo, Massimiliano Pontil
Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport. CoRR (2021)
Leonardo Cella, Massimiliano Pontil
Multi-task and meta-learning with sparse linear bandits. UAI (2021)
Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto
The Role of Global Labels in Few-Shot Classification and How to Infer Them. NeurIPS (2021)
Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil
A Gang of Adversarial Bandits. NeurIPS (2021)
Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil
Multitask Online Mirror Descent. CoRR (2021)
Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
Conditional Meta-Learning of Linear Representations. CoRR (2021)
Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil
Robust Unsupervised Learning via L-statistic Minimization. ICML (2021)
Andreas Maurer, Massimiliano Pontil
Some Hoeffding- and Bernstein-type Concentration Inequalities. CoRR (2021)
Henry Gouk, Timothy M. Hospedales, Massimiliano Pontil
Distance-Based Regularisation of Deep Networks for Fine-Tuning. ICLR (2021)
Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov
Distributed Zero-Order Optimization under Adversarial Noise. NeurIPS (2021)
Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. NeurIPS (2020)
Luca Oneto, Michele Donini, Massimiliano Pontil
General Fair Empirical Risk Minimization. IJCNN (2020)
Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi
Marthe: Scheduling the Learning Rate Via Online Hypergradients. IJCAI (2020)
Luca Oneto, Michele Donini, Massimiliano Pontil, Andreas Maurer
Learning Fair and Transferable Representations with Theoretical Guarantees. DSAA (2020)
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
Convergence Properties of Stochastic Hypergradients. CoRR (2020)
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
Fair regression via plug-in estimator and recalibration with statistical guarantees. NeurIPS (2020)
Marco Fiorucci, Marina Khoroshiltseva, Massimiliano Pontil, Arianna Traviglia, Alessio Del Bue, Stuart James
Machine Learning for Cultural Heritage: A Survey. Pattern Recognit. Lett. 133 (2020)
Giulia Denevi, Massimiliano Pontil, Dimitrios Stamos
Online Parameter-Free Learning of Multiple Low Variance Tasks. UAI (2020)
Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning. CoRR (2020)
Giulia Denevi, Dimitris Stamos, Massimiliano Pontil
Online Parameter-Free Learning of Multiple Low Variance Tasks. CoRR (2020)
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
Fair Regression with Wasserstein Barycenters. CoRR (2020)
Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits. CoRR (2020)
Luca Oneto, Michele Donini, Massimiliano Pontil, John Shawe-Taylor
Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy. Neurocomputing 416 (2020)
Giulia Luise, Massimiliano Pontil, Carlo Ciliberto
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning. CoRR (2020)
Leonardo Cella, Claudio Gentile, Massimiliano Pontil
Online Model Selection: a Rested Bandit Formulation. CoRR (2020)
Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
Meta-learning with Stochastic Linear Bandits. ICML (2020)
Henry Gouk, Timothy M. Hospedales, Massimiliano Pontil
Distance-Based Regularisation of Deep Networks for Fine-Tuning. CoRR (2020)
Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport. CoRR (2020)
Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini
Approximating Hamiltonian dynamics with the Nyström method. Quantum 4 (2020)
Jordan Frécon, Saverio Salzo, Massimiliano Pontil
Unveiling Groups of Related Tasks in Multi - Task Learning. ICPR (2020)
Feliks Hibraj, Marcello Pelillo, Saverio Salzo, Massimiliano Pontil
Efficient Tensor Kernel methods for sparse regression. CoRR (2020)
Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo
On the Iteration Complexity of Hypergradient Computation. CoRR (2020)
Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning. NeurIPS (2020)
Andreas Maurer, Massimiliano Pontil
Estimating weighted areas under the ROC curve. NeurIPS (2020)
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
Fair regression with Wasserstein barycenters. NeurIPS (2020)
Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo
On the Iteration Complexity of Hypergradient Computation. ICML (2020)
Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits. NeurIPS (2020)
Andreas Maurer, Daniela A. Parletta, Andrea Paudice, Massimiliano Pontil
A Perturbation Resilient Framework for Unsupervised Learning. CoRR (2020)
Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
Meta-learning with Stochastic Linear Bandits. CoRR (2020)
Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil
Taking Advantage of Multitask Learning for Fair Classification. AIES (2019)
Giulia Luise, Dimitris Stamos, Massimiliano Pontil, Carlo Ciliberto
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. CoRR (2019)
Octavio Antonio Villarreal-Magaña, Victor Barasuol, Marco Camurri, Luca Franceschi, Michele Focchi, Massimiliano Pontil, Darwin G. Caldwell, Claudio Semini
Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs. IEEE Robotics Autom. Lett. 4 (2) (2019)
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He
Learning Discrete Structures for Graph Neural Networks. ICML (2019)
Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification. NeurIPS (2019)
Luca Oneto, Michele Donini, Massimiliano Pontil
General Fair Empirical Risk Minimization. CoRR (2019)
Luca Oneto, Michele Donini, Massimiliano Pontil
PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. ESANN (2019)
Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. ICML (2019)
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization. ICML (2019)
Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil
Online-Within-Online Meta-Learning. NeurIPS (2019)
Patrick L. Combettes, Andrew M. McDonald, Charles A. Micchelli, Massimiliano Pontil
Learning with optimal interpolation norms. Numer. Algorithms 81 (2) (2019)
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He
Learning Discrete Structures for Graph Neural Networks. CoRR (2019)
Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. NeurIPS (2019)
Michele Donini, João M. Monteiro, Massimiliano Pontil, Tim Hahn, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda
Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. NeuroImage 195 (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Massimiliano Pontil