Massimiliano Pontil

287 papers A* 64A 17B 17C 2Journal 146Unranked 40

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Randomized Algorithm to Solve Reduced Rank Operator Regression. SIAM J. Math. Data Sci. Giacomo Turri, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil
2026	J	`jnl`	AdaGrad-Diff: A New Version of the Adaptive Gradient Algorithm. CoRR Matia Bojovic, Saverio Salzo, Massimiliano Pontil
2026	J	`jnl`	Schedule-Robust Continual Learning. IEEE Trans. Pattern Anal. Mach. Intell. Ruohan Wang, Marco Ciccone, Massimiliano Pontil, Carlo Ciliberto
2026	J	`jnl`	SpectraFormer: an Attention-Based Raman Unmixing Tool for Accessing the Graphene Buffer-Layer Signature on SiC. CoRR Dmitriy Poteryayev, Pietro Novelli, Annalisa Coriolano, Riccardo Dettori, Valentina Tozzini, Fabio Beltram, Massimiliano Pontil, Antonio Rossi, Stiven Forti, Camilla Coletti
2026	J	`jnl`	Toeplitz Based Spectral Methods for Data-driven Dynamical Systems. CoRR Vladimir R. Kostic, Karim Lounici, Massimiliano Pontil
2025	A*	`conf`	A Bregman Proximal Viewpoint on Neural Operators. ICML Abdel-Rahim Mezidi, Jordan Patracone, Saverio Salzo, Amaury Habrard, Massimiliano Pontil, Rémi Emonet, Marc Sebban
2025	A	`conf`	An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications. AISTATS Erfan Mirzaei, Andreas Maurer, Vladimir R. Kostic, Massimiliano Pontil
2025	J	`jnl`	An Empirical Bernstein Inequality for Dependent Data in Hilbert Spaces and Applications. CoRR Erfan Mirzaei, Andreas Maurer, Vladimir R. Kostic, Massimiliano Pontil
2025	J	`jnl`	DeltaProduct: Increasing the Expressivity of DeltaNet Through Products of Householders. CoRR Julien Siems, Timur Carstensen, Arber Zela, Frank Hutter, Massimiliano Pontil, Riccardo Grazzi
2025	J	`jnl`	Equivariant Representation Learning for Symmetry-Aware Inference with Guarantees. CoRR Daniel Felipe Ordoñez Apraez, Vladimir Kostic, Alek Fröhlich, Vivien Brandt, Karim Lounici, Massimiliano Pontil
2025	J	`jnl`	Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime. CoRR Andreas Maurer, Erfan Mirzaei, Massimiliano Pontil
2025	J	`jnl`	Hyperparameter Optimization in Machine Learning. Found. Trends Mach. Learn. Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi
2025	A*	`conf`	Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups. ICML Vladimir R. Kostic, Karim Lounici, Hélène Halconruy, Timothée Devergne, Pietro Novelli, Massimiliano Pontil
2025	J	`jnl`	Morphological symmetries in robotics. Int. J. Robotics Res. Daniel Felipe Ordoñez Apraez, Giulio Turrisi, Vladimir Kostic, Mario Martín, Antonio Agudo, Francesc Moreno-Noguer, Massimiliano Pontil, Claudio Semini, Carlos Mastalli
2025	J	`jnl`	Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression. CoRR Dimitri Meunier, Jakub Wornbard, Vladimir R. Kostic, Antoine Moulin, Alek Fröhlich, Karim Lounici, Massimiliano Pontil, Arthur Gretton
2025	J	`jnl`	Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems. CoRR Giacomo Turri, Luigi Bonati, Kai Zhu, Massimiliano Pontil, Pietro Novelli
2025	J	`jnl`	Toward Scalable and Valid Conditional Independence Testing with Spectral Representations. CoRR Alek Fröhlich, Vladimir Kostic, Karim Lounici, Daniel Perazzo, Massimiliano Pontil
2025	A*	`conf`	Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues. ICLR Riccardo Grazzi, Julien Siems, Arber Zela, Jörg K. H. Franke, Frank Hutter, Massimiliano Pontil
2025	J	`jnl`	kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning. CoRR Giacomo Turri, Grégoire Pacreau, Giacomo Meanti, Timothée Devergne, Daniel Ordonez, Erfan Mirzaei, Bruno Belucci, Karim Lounici, Vladimir Kostic, Massimiliano Pontil, Pietro Novelli
2024	J	`jnl`	Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials Growth. CoRR Leonardo Sabattini, Annalisa Coriolano, Corneel Casert, Stiven Forti, Edward S. Barnard, Fabio Beltram, Massimiliano Pontil, Stephen Whitelam, Camilla Coletti, Antonio Rossi
2024	A*	`conf`	Consistent Long-Term Forecasting of Ergodic Dynamical Systems. ICML Vladimir R. Kostic, Karim Lounici, Prune Inzerilli, Pietro Novelli, Massimiliano Pontil
2024	J	`jnl`	Contextual Continuum Bandits: Static Versus Dynamic Regret. CoRR Arya Akhavan, Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov
2024	—	`conf`	Dynamics harmonic analysis of robotic systems: Application in data-driven Koopman modelling. L4DC Daniel Felipe Ordoñez Apraez, Vladimir Kostic, Giulio Turrisi, Pietro Novelli, Carlos Mastalli, Claudio Semini, Massimiliano Pontil
2024	A*	`conf`	From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach. NeurIPS Timothée Devergne, Vladimir Kostic, Michele Parrinello, Massimiliano Pontil
2024	J	`jnl`	From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach. CoRR Timothee Devergne, Vladimir Kostic, Michele Parrinello, Massimiliano Pontil
2024	J	`jnl`	Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm. J. Mach. Learn. Res. Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov
2024	J	`jnl`	High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise]. SIAM J. Math. Data Sci. Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil, Saverio Salzo
2024	J	`jnl`	Hyperparameter Optimization in Machine Learning. CoRR Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi
2024	J	`jnl`	Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups. CoRR Vladimir R. Kostic, Karim Lounici, Hélène Halconruy, Timothee Devergne, Pietro Novelli, Massimiliano Pontil
2024	A*	`conf`	Learning invariant representations of time-homogeneous stochastic dynamical systems. ICLR Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil
2024	A*	`conf`	Learning the Infinitesimal Generator of Stochastic Diffusion Processes. NeurIPS Vladimir Kostic, Hélène Halconruy, Timothée Devergne, Karim Lounici, Massimiliano Pontil
2024	J	`jnl`	Learning the Infinitesimal Generator of Stochastic Diffusion Processes. CoRR Vladimir R. Kostic, Karim Lounici, Hélène Halconruy, Timothee Devergne, Massimiliano Pontil
2024	A	`conf`	Leveraging Symmetry in RL-based Legged Locomotion Control. IROS Zhi Su, Xiaoyu Huang, Daniel Felipe Ordoñez Apraez, Yunfei Li, Zhongyu Li, Qiayuan Liao, Giulio Turrisi, Massimiliano Pontil, Claudio Semini, Yi Wu, Koushil Sreenath
2024	J	`jnl`	Leveraging Symmetry in RL-based Legged Locomotion Control. CoRR Zhi Su, Xiaoyu Huang, Daniel Felipe Ordoñez Apraez, Yunfei Li, Zhongyu Li, Qiayuan Liao, Giulio Turrisi, Massimiliano Pontil, Claudio Semini, Yi Wu, Koushil Sreenath
2024	J	`jnl`	Morphological Symmetries in Robotics. CoRR Daniel Felipe Ordoñez Apraez, Giulio Turrisi, Vladimir Kostic, Mario Martín, Antonio Agudo, Francesc Moreno-Noguer, Massimiliano Pontil, Claudio Semini, Carlos Mastalli
2024	J	`jnl`	Multi-Rater Consensus Learning for Modeling Multiple Sparse Ratings of Affective Behaviour. IEEE Trans. Affect. Comput. Luca Romeo, Temitayo A. Olugbade, Massimiliano Pontil, Nadia Bianchi-Berthouze
2024	J	`jnl`	Neural Conditional Probability for Inference. CoRR Vladimir R. Kostic, Karim Lounici, Grégoire Pacreau, Pietro Novelli, Giacomo Turri, Massimiliano Pontil
2024	A*	`conf`	Neural Conditional Probability for Uncertainty Quantification. NeurIPS Vladimir Kostic, Grégoire Pacreau, Giacomo Turri, Pietro Novelli, Karim Lounici, Massimiliano Pontil
2024	A*	`conf`	Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates. ICML Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
2024	J	`jnl`	Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates. CoRR Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
2024	A*	`conf`	Operator World Models for Reinforcement Learning. NeurIPS Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo Ciliberto
2024	J	`jnl`	Operator World Models for Reinforcement Learning. CoRR Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo Ciliberto
2024	J	`jnl`	QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex. CoRR Salvatore Calcagno, Isaak Kavasidis, Simone Palazzo, Marco Brondi, Luca Sità, Giacomo Turri, Daniela Giordano, Vladimir R. Kostic, Tommaso Fellin, Massimiliano Pontil, Concetto Spampinato
2024	J	`jnl`	Robust Meta-Representation Learning via Global Label Inference and Classification. IEEE Trans. Pattern Anal. Mach. Intell. Ruohan Wang, John Isak Texas Falk, Massimiliano Pontil, Carlo Ciliberto
2024	J	`jnl`	Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues. CoRR Riccardo Grazzi, Julien Siems, Jörg K. H. Franke, Arber Zela, Frank Hutter, Massimiliano Pontil
2023	J	`jnl`	A randomized algorithm to solve reduced rank operator regression. CoRR Giacomo Turri, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil
2023	J	`jnl`	Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. J. Mach. Learn. Res. Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
2023	J	`jnl`	Consistent Long-Term Forecasting of Ergodic Dynamical Systems. CoRR Prune Inzerilli, Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil
2023	J	`jnl`	Deep projection networks for learning time-homogeneous dynamical systems. CoRR Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil
2023	J	`jnl`	Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling. CoRR Daniel Felipe Ordoñez Apraez, Vladimir Kostic, Giulio Turrisi, Pietro Novelli, Carlos Mastalli, Claudio Semini, Massimiliano Pontil
2023	A*	`conf`	Estimating Koopman operators with sketching to provably learn large scale dynamical systems. NeurIPS Giacomo Meanti, Antoine Chatalic, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco
2023	J	`jnl`	Estimating Koopman operators with sketching to provably learn large scale dynamical systems. CoRR Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic, Pietro Novelli, Massimiliano Pontil, Lorenzo Rosasco
2023	J	`jnl`	Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues. CoRR Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil
2023	A	`conf`	Multi-task Representation Learning with Stochastic Linear Bandits. AISTATS Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil
2023	A*	`conf`	Sharp Spectral Rates for Koopman Operator Learning. NeurIPS Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil
2023	A*	`conf`	Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. NeurIPS John Isak Texas Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil
2023	J	`jnl`	Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. CoRR John Isak Texas Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil
2022	A*	`conf`	A gradient estimator via L1-randomization for online zero-order optimization with two point feedback. NeurIPS Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov
2022	A*	`conf`	Batch Greenkhorn Algorithm for Entropic-Regularized Multimarginal Optimal Transport: Linear Rate of Convergence and Iteration Complexity. ICML Vladimir R. Kostic, Saverio Salzo, Massimiliano Pontil
2022	J	`jnl`	Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. CoRR Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
2022	A*	`conf`	Bregman Neural Networks. ICML Jordan Frécon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo
2022	J	`jnl`	Characterizing metastable states with the help of machine learning. CoRR Pietro Novelli, Luigi Bonati, Massimiliano Pontil, Michele Parrinello
2022	A*	`conf`	Conditional Meta-Learning of Linear Representations. NeurIPS Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
2022	A*	`conf`	Distribution Regression with Sliced Wasserstein Kernels. ICML Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto
2022	J	`jnl`	Distribution Regression with Sliced Wasserstein Kernels. CoRR Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto
2022	A*	`conf`	Group Meritocratic Fairness in Linear Contextual Bandits. NeurIPS Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil
2022	J	`jnl`	Group Meritocratic Fairness in Linear Contextual Bandits. CoRR Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil
2022	A	`conf`	Implicit kernel meta-learning using kernel integral forms. UAI John Isak Texas Falk, Carlo Ciliberto, Massimiliano Pontil
2022	A*	`conf`	Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. NeurIPS Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil
2022	J	`jnl`	Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces. CoRR Vladimir Kostic, Pietro Novelli, Andreas Maurer, Carlo Ciliberto, Lorenzo Rosasco, Massimiliano Pontil
2022	J	`jnl`	Meta Representation Learning with Contextual Linear Bandits. CoRR Leonardo Cella, Karim Lounici, Massimiliano Pontil
2022	A	`conf`	Multi-source domain adaptation via weighted joint distributions optimal transport. UAI Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil
2022	J	`jnl`	Multi-task Representation Learning with Stochastic Linear Bandits. CoRR Leonardo Cella, Karim Lounici, Massimiliano Pontil
2022	J	`jnl`	Multiple Instance Learning for Emotion Recognition Using Physiological Signals. IEEE Trans. Affect. Comput. Luca Romeo, Andrea Cavallo, Lucia Pepa, Nadia Bianchi-Berthouze, Massimiliano Pontil
2022	J	`jnl`	Multitask Online Mirror Descent. Trans. Mach. Learn. Res. Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil
2022	J	`jnl`	Robust Meta-Representation Learning via Global Label Inference and Classification. CoRR Ruohan Wang, John Isak Texas Falk, Massimiliano Pontil, Carlo Ciliberto
2022	J	`jnl`	Schedule-Robust Online Continual Learning. CoRR Ruohan Wang, Marco Ciccone, Giulia Luise, Andrew Yapp, Massimiliano Pontil, Carlo Ciliberto
2021	A*	`conf`	A Gang of Adversarial Bandits. NeurIPS Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil
2021	A*	`conf`	Best Model Identification: A Rested Bandit Formulation. ICML Leonardo Cella, Massimiliano Pontil, Claudio Gentile
2021	A*	`conf`	Concentration inequalities under sub-Gaussian and sub-exponential conditions. NeurIPS Andreas Maurer, Massimiliano Pontil
2021	J	`jnl`	Conditional Meta-Learning of Linear Representations. CoRR Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
2021	A	`conf`	Convergence Properties of Stochastic Hypergradients. AISTATS Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
2021	J	`jnl`	Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport. CoRR Vladimir Kostic, Saverio Salzo, Massimiliano Pontil
2021	A*	`conf`	Distance-Based Regularisation of Deep Networks for Fine-Tuning. ICLR Henry Gouk, Timothy M. Hospedales, Massimiliano Pontil
2021	A*	`conf`	Distributed Zero-Order Optimization under Adversarial Noise. NeurIPS Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov
2021	A	`conf`	Multi-task and meta-learning with sparse linear bandits. UAI Leonardo Cella, Massimiliano Pontil
2021	J	`jnl`	Multitask Online Mirror Descent. CoRR Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil
2021	A*	`conf`	Robust Unsupervised Learning via L-statistic Minimization. ICML Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil
2021	J	`jnl`	Some Hoeffding- and Bernstein-type Concentration Inequalities. CoRR Andreas Maurer, Massimiliano Pontil
2021	A*	`conf`	The Role of Global Labels in Few-Shot Classification and How to Infer Them. NeurIPS Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto
2021	J	`jnl`	The Role of Global Labels in Few-Shot Classification and How to Infer Them. CoRR Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto
2020	J	`jnl`	A Perturbation Resilient Framework for Unsupervised Learning. CoRR Andreas Maurer, Daniela A. Parletta, Andrea Paudice, Massimiliano Pontil
2020	J	`jnl`	Approximating Hamiltonian dynamics with the Nyström method. Quantum Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini
2020	J	`jnl`	Convergence Properties of Stochastic Hypergradients. CoRR Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
2020	J	`jnl`	Distance-Based Regularisation of Deep Networks for Fine-Tuning. CoRR Henry Gouk, Timothy M. Hospedales, Massimiliano Pontil
2020	J	`jnl`	Efficient Tensor Kernel methods for sparse regression. CoRR Feliks Hibraj, Marcello Pelillo, Saverio Salzo, Massimiliano Pontil
2020	A*	`conf`	Estimating weighted areas under the ROC curve. NeurIPS Andreas Maurer, Massimiliano Pontil
2020	A*	`conf`	Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits. NeurIPS Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov
2020	J	`jnl`	Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits. CoRR Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov
2020	A*	`conf`	Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning. NeurIPS Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil
2020	J	`jnl`	Fair Regression with Wasserstein Barycenters. CoRR Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
2020	A*	`conf`	Fair regression via plug-in estimator and recalibration with statistical guarantees. NeurIPS Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
2020	A*	`conf`	Fair regression with Wasserstein barycenters. NeurIPS Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
2020	B	`conf`	General Fair Empirical Risk Minimization. IJCNN Luca Oneto, Michele Donini, Massimiliano Pontil
2020	J	`jnl`	Generalization Properties of Optimal Transport GANs with Latent Distribution Learning. CoRR Giulia Luise, Massimiliano Pontil, Carlo Ciliberto
2020	B	`conf`	Learning Fair and Transferable Representations with Theoretical Guarantees. DSAA Luca Oneto, Michele Donini, Massimiliano Pontil, Andreas Maurer
2020	J	`jnl`	Machine Learning for Cultural Heritage: A Survey. Pattern Recognit. Lett. Marco Fiorucci, Marina Khoroshiltseva, Massimiliano Pontil, Arianna Traviglia, Alessio Del Bue, Stuart James
2020	A*	`conf`	Marthe: Scheduling the Learning Rate Via Online Hypergradients. IJCAI Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi
2020	A*	`conf`	Meta-learning with Stochastic Linear Bandits. ICML Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
2020	J	`jnl`	Meta-learning with Stochastic Linear Bandits. CoRR Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil
2020	J	`jnl`	Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport. CoRR Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil
2020	A*	`conf`	On the Iteration Complexity of Hypergradient Computation. ICML Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo
2020	J	`jnl`	On the Iteration Complexity of Hypergradient Computation. CoRR Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo
2020	J	`jnl`	Online Model Selection: a Rested Bandit Formulation. CoRR Leonardo Cella, Claudio Gentile, Massimiliano Pontil
2020	A	`conf`	Online Parameter-Free Learning of Multiple Low Variance Tasks. UAI Giulia Denevi, Massimiliano Pontil, Dimitrios Stamos
2020	J	`jnl`	Online Parameter-Free Learning of Multiple Low Variance Tasks. CoRR Giulia Denevi, Dimitris Stamos, Massimiliano Pontil
2020	J	`jnl`	Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy. Neurocomputing Luca Oneto, Michele Donini, Massimiliano Pontil, John Shawe-Taylor
2020	A*	`conf`	The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning. NeurIPS Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
2020	J	`jnl`	The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning. CoRR Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
2020	B	`conf`	Unveiling Groups of Related Tasks in Multi - Task Learning. ICPR Jordan Frécon, Saverio Salzo, Massimiliano Pontil
2019	J	`jnl`	Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. NeuroImage Michele Donini, João M. Monteiro, Massimiliano Pontil, Tim Hahn, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda
2019	J	`jnl`	Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs. IEEE Robotics Autom. Lett. Octavio Antonio Villarreal-Magaña, Victor Barasuol, Marco Camurri, Luca Franceschi, Michele Focchi, Massimiliano Pontil, Darwin G. Caldwell, Claudio Semini
2019	J	`jnl`	General Fair Empirical Risk Minimization. CoRR Luca Oneto, Michele Donini, Massimiliano Pontil
2019	A*	`conf`	Learning Discrete Structures for Graph Neural Networks. ICML Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He
2019	J	`jnl`	Learning Discrete Structures for Graph Neural Networks. CoRR Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He
2019	J	`jnl`	Learning Fair and Transferable Representations. CoRR Luca Oneto, Michele Donini, Andreas Maurer, Massimiliano Pontil
2019	J	`jnl`	Learning with optimal interpolation norms. Numer. Algorithms Patrick L. Combettes, Andrew M. McDonald, Charles A. Micchelli, Massimiliano Pontil
2019	A*	`conf`	Learning-to-Learn Stochastic Gradient Descent with Biased Regularization. ICML Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil
2019	J	`jnl`	Learning-to-Learn Stochastic Gradient Descent with Biased Regularization. CoRR Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil
2019	A*	`conf`	Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification. NeurIPS Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil
2019	A*	`conf`	Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. ICML Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto
2019	J	`jnl`	Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. CoRR Giulia Luise, Dimitris Stamos, Massimiliano Pontil, Carlo Ciliberto
2019	A*	`conf`	Online-Within-Online Meta-Learning. NeurIPS Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil
2019	B	`conf`	PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. ESANN Luca Oneto, Michele Donini, Massimiliano Pontil
2019	J	`jnl`	Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm. CoRR Michele Donini, Luca Franceschi, Massimiliano Pontil, Orchid Majumder, Paolo Frasconi
2019	A*	`conf`	Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. NeurIPS Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto
2019	J	`jnl`	Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. CoRR Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto
2019	C	`conf`	Taking Advantage of Multitask Learning for Fair Classification. AIES Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil
2019	A*	`conf`	Uniform concentration and symmetrization for weak interactions. COLT Andreas Maurer, Massimiliano Pontil
2019	J	`jnl`	Uniform concentration and symmetrization for weak interactions. CoRR Andreas Maurer, Massimiliano Pontil
2018	J	`jnl`	Approximating Hamiltonian dynamics with the Nyström method. CoRR Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini
2018	A*	`conf`	Bilevel Programming for Hyperparameter Optimization and Meta-Learning. ICML Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil
2018	J	`jnl`	Bilevel Programming for Hyperparameter Optimization and Meta-Learning. CoRR Luca Franceschi, Paolo Frasconi, Saverio Salzo, Massimiliano Pontil
2018	A*	`conf`	Bilevel learning of the Group Lasso structure. NeurIPS Jordan Frécon, Saverio Salzo, Massimiliano Pontil
2018	A*	`conf`	Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. NeurIPS Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto
2018	J	`jnl`	Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. CoRR Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto
2018	A*	`conf`	Empirical Risk Minimization Under Fairness Constraints. NeurIPS Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
2018	J	`jnl`	Empirical Risk Minimization under Fairness Constraints. CoRR Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
2018	A*	`conf`	Empirical bounds for functions with weak interactions. COLT Andreas Maurer, Massimiliano Pontil
2018	J	`jnl`	Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning. CoRR Luca Franceschi, Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo, Paolo Frasconi
2018	J	`jnl`	Fast and Continuous Foothold Adaptation for Dynamic Locomotion through Convolutional Neural Networks. CoRR Octavio Antonio Villarreal-Magaña, Victor Barasuol, Marco Camurri, Michele Focchi, Luca Franceschi, Massimiliano Pontil, Darwin G. Caldwell, Claudio Semini
2018	A	`conf`	Incremental Learning-to-Learn with Statistical Guarantees. UAI Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
2018	J	`jnl`	Incremental Learning-to-Learn with Statistical Guarantees. CoRR Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
2018	J	`jnl`	Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning. IEEE Trans. Pattern Anal. Mach. Intell. Peixi Peng, Yonghong Tian, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Tiejun Huang
2018	A*	`conf`	Learning To Learn Around A Common Mean. NeurIPS Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
2018	J	`jnl`	Learning local metrics from pairwise similarity data. Pattern Recognit. Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil
2018	J	`jnl`	Taking Advantage of Multitask Learning for Fair Classification. CoRR Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil
2017	J	`jnl`	A Bridge Between Hyperparameter Optimization and Larning-to-learn. CoRR Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
2017	A	`conf`	A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion. INTERSPEECH Leonardo Badino, Luca Franceschi, Raman Arora, Michele Donini, Massimiliano Pontil
2017	—	`conf`	Consistent Multitask Learning with Nonlinear Output Relations. NIPS Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil
2017	J	`jnl`	Consistent Multitask Learning with Nonlinear Output Relations. CoRR Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil
2017	A*	`conf`	Forward and Reverse Gradient-Based Hyperparameter Optimization. ICML Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
2017	—	`conf`	On Hyperparameter Optimization in Learning Systems. ICLR (Workshop) Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil
2017	J	`jnl`	Quantum machine learning: a classical perspective. CoRR Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig
2017	J	`jnl`	Reexamining Low Rank Matrix Factorization for Trace Norm Regularization. CoRR Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil
2017	A	`conf`	Regret Bounds for Lifelong Learning. AISTATS Pierre Alquier, The Tien Mai, Massimiliano Pontil
2016	—	`conf`	A multimodal multiple kernel learning approach to Alzheimer's disease detection. MLSP Michele Donini, João M. Monteiro, Massimiliano Pontil, John Shawe-Taylor, Janaina Mourão Miranda
2016	J	`jnl`	Bounds for Vector-Valued Function Estimation. CoRR Andreas Maurer, Massimiliano Pontil
2016	B	`conf`	Distributed variance regularized Multitask Learning. IJCNN Michele Donini, David Martínez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil
2016	J	`jnl`	Fitting Spectral Decay with the $k$-Support Norm. CoRR Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
2016	A	`conf`	Fitting Spectral Decay with the k-Support Norm. AISTATS Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
2016	—	`conf`	Mistake Bounds for Binary Matrix Completion. NIPS Mark Herbster, Stephen Pasteris, Massimiliano Pontil
2016	J	`jnl`	New Perspectives on k-Support and Cluster Norms. J. Mach. Learn. Res. Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
2016	J	`jnl`	Regret Bounds for Lifelong Learning. CoRR Pierre Alquier, The Tien Mai, Massimiliano Pontil
2016	C	`conf`	Similarity Function Learning with Data Uncertainty. ICPRAM Julien Bohné, Sylvain Colin, Stéphane Gentric, Massimiliano Pontil
2016	J	`jnl`	The Benefit of Multitask Representation Learning. J. Mach. Learn. Res. Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
2016	A*	`conf`	Unsupervised Cross-Dataset Transfer Learning for Person Re-identification. CVPR Peixi Peng, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian
2015	A*	`conf`	Learning with dataset bias in latent subcategory models. CVPR Dimitris Stamos, Samuele Martelli, Moin Nabi, Andrew M. McDonald, Vittorio Murino, Massimiliano Pontil
2015	J	`jnl`	Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). Dagstuhl Reports Trevor Darrell, Marius Kloft, Massimiliano Pontil, Gunnar Rätsch, Erik Rodner
2015	J	`jnl`	New Perspectives on $k$-Support and Cluster Norms. CoRR Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
2015	J	`jnl`	Predicting a switching sequence of graph labelings. J. Mach. Learn. Res. Mark Herbster, Stephen Pasteris, Massimiliano Pontil
2015	J	`jnl`	The Benefit of Multitask Representation Learning. CoRR Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
2014	J	`jnl`	A regularized matrix factorization approach to induce structured sparse-low-rank solutions in the EEG inverse problem. EURASIP J. Adv. Signal Process. Jair Montoya-Martínez, Antonio Artés-Rodríguez, Massimiliano Pontil, Lars Kai Hansen
2014	A*	`conf`	An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning. COLT Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
2014	J	`jnl`	An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning. CoRR Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
2014	—	`conf`	Large Margin Local Metric Learning. ECCV (2) Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil
2014	—	`conf`	Spectral k-Support Norm Regularization. NIPS Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
2014	—	`conf`	Structured sparse-low rank matrix factorization for the EEG inverse problem. CIP Jair Montoya-Martínez, Antonio Artés-Rodríguez, Massimiliano Pontil
2013	—	`conf`	A New Convex Relaxation for Tensor Completion. NIPS Bernardino Romera-Paredes, Massimiliano Pontil
2013	J	`jnl`	A New Convex Relaxation for Tensor Completion. CoRR Bernardino Romera-Paredes, Massimiliano Pontil
2013	A*	`conf`	Excess risk bounds for multitask learning with trace norm regularization. COLT Massimiliano Pontil, Andreas Maurer
2013	—	`conf`	Multi-task Averaging via Task Clustering. SIMBAD David Martínez-Rego, Massimiliano Pontil
2013	—	`conf`	Multilinear Multitask Learning. ICML (3) Bernardino Romera-Paredes, Hane Aung, Nadia Bianchi-Berthouze, Massimiliano Pontil
2013	J	`jnl`	On Sparsity Inducing Regularization Methods for Machine Learning CoRR Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil
2013	—	`conf`	On Sparsity Inducing Regularization Methods for Machine Learning. Empirical Inference Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil
2013	J	`jnl`	Regularizers for structured sparsity. Adv. Comput. Math. Charles A. Micchelli, Jean Morales, Massimiliano Pontil
2013	—	`conf`	Sparse coding for multitask and transfer learning. ICML (2) Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
2013	B	`conf`	Transfer learning to account for idiosyncrasy in face and body expressions. FG Bernardino Romera-Paredes, Min S. H. Aung, Massimiliano Pontil, Nadia Bianchi-Berthouze, Amanda C. de C. Williams, Paul J. Watson
2012	A	`conf`	A General Framework for Structured Sparsity via Proximal Optimization. AISTATS Luca Baldassarre, Jean Morales, Andreas Argyriou, Massimiliano Pontil
2012	J	`jnl`	Conditional mean embeddings as regressors - supplementary CoRR Steffen Grünewälder, Guy Lever, Luca Baldassarre, Sam Patterson, Arthur Gretton, Massimiliano Pontil
2012	A*	`conf`	Conditional mean embeddings as regressors. ICML Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil
2012	J	`jnl`	Excess risk bounds for multitask learning with trace norm regularization CoRR Andreas Maurer, Massimiliano Pontil
2012	A	`conf`	Exploiting Unrelated Tasks in Multi-Task Learning. AISTATS Bernardino Romera-Paredes, Andreas Argyriou, Nadia Berthouze, Massimiliano Pontil
2012	J	`jnl`	Modelling transition dynamics in MDPs with RKHS embeddings CoRR Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton
2012	A*	`conf`	Modelling transition dynamics in MDPs with RKHS embeddings. ICML Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton
2012	—	`conf`	Optimal kernel choice for large-scale two-sample tests. NIPS Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu
2012	J	`jnl`	PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinform. David T. Jones, Daniel W. A. Buchan, Domenico Cozzetto, Massimiliano Pontil
2012	J	`jnl`	Sparse coding for multitask and transfer learning CoRR Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
2012	—	`conf`	Structured Sparsity Models for Brain Decoding from fMRI Data. PRNI Luca Baldassarre, Janaina Mourão Miranda, Massimiliano Pontil
2012	J	`jnl`	Structured Sparsity and Generalization. J. Mach. Learn. Res. Andreas Maurer, Massimiliano Pontil
2012	—	`conf`	Structured sparsity regularization approach to the EEG inverse problem. CIP Jair Montoya-Martínez, Antonio Artés-Rodríguez, Lars Kai Hansen, Massimiliano Pontil
2012	—	`conf`	Transfer learning in a heterogeneous environment. CIP Andreas Maurer, Massimiliano Pontil
2011	J	`jnl`	A General Framework for Structured Sparsity via Proximal Optimization CoRR Andreas Argyriou, Luca Baldassarre, Jean Morales, Massimiliano Pontil
2011	J	`jnl`	A tale of many cities: universal patterns in human urban mobility CoRR Anastasios Noulas, Salvatore Scellato, Renaud Lambiotte, Massimiliano Pontil, Cecilia Mascolo
2011	A	`conf`	An Empirical Study of Geographic User Activity Patterns in Foursquare. ICWSM Anastasios Noulas, Salvatore Scellato, Cecilia Mascolo, Massimiliano Pontil
2011	J	`jnl`	Efficient First Order Methods for Linear Composite Regularizers CoRR Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Lixin Shen, Yuesheng Xu
2011	—	`conf`	Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks. The Social Mobile Web Anastasios Noulas, Salvatore Scellato, Cecilia Mascolo, Massimiliano Pontil
2011	J	`jnl`	Structured Sparsity and Generalization CoRR Andreas Maurer, Massimiliano Pontil
2010	—	`conf`	A Family of Penalty Functions for Structured Sparsity. NIPS Charles A. Micchelli, Jean Morales, Massimiliano Pontil
2010	J	`jnl`	K -Dimensional Coding Schemes in Hilbert Spaces. IEEE Trans. Inf. Theory Augusto Maurer, Massimiliano Pontil
2010	J	`jnl`	On Spectral Learning. J. Mach. Learn. Res. Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil
2009	A*	`conf`	Empirical Bernstein Bounds and Sample-Variance Penalization. COLT Andreas Maurer, Massimiliano Pontil
2009	J	`jnl`	Entropy conditions for Adv. Comput. Math. Andrea Caponnetto, Ernesto De Vito, Massimiliano Pontil
2009	—	`ch.`	Machine Learning Techniques for Biometrics. Handbook of Remote Biometrics Francesca Odone, Massimiliano Pontil, Alessandro Verri
2009	J	`jnl`	Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinform. Stefano Lise, Cédric Archambeau, Massimiliano Pontil, David T. Jones
2009	A*	`conf`	Taking Advantage of Sparsity in Multi-Task Learning. COLT Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov, Sara A. van de Geer
2009	J	`jnl`	When Is There a Representer Theorem? Vector Versus Matrix Regularizers. J. Mach. Learn. Res. Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil
2008	B	`conf`	A Uniform Lower Error Bound for Half-Space Learning. ALT Andreas Maurer, Massimiliano Pontil
2008	—	`conf`	An Algorithm for Transfer Learning in a Heterogeneous Environment. ECML/PKDD (1) Andreas Argyriou, Andreas Maurer, Massimiliano Pontil
2008	J	`jnl`	Convex multi-task feature learning. Mach. Learn. Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil
2008	—	`conf`	Fast Prediction on a Tree. NIPS Mark Herbster, Massimiliano Pontil, Sergio Rojas Galeano
2008	B	`conf`	Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces. ALT Andreas Maurer, Massimiliano Pontil
2008	J	`jnl`	Online Gradient Descent Learning Algorithms. Found. Comput. Math. Yiming Ying, Massimiliano Pontil
2008	—	`conf`	Online Prediction on Large Diameter Graphs. NIPS Mark Herbster, Guy Lever, Massimiliano Pontil
2008	J	`jnl`	Universal Multi-Task Kernels. J. Mach. Learn. Res. Andrea Caponnetto, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying
2008	J	`jnl`	When is there a representer theorem? Vector versus matrix regularizers CoRR Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil
2007	—	`conf`	A Spectral Regularization Framework for Multi-Task Structure Learning. NIPS Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying
2007	J	`jnl`	Feature space perspectives for learning the kernel. Mach. Learn. Charles A. Micchelli, Massimiliano Pontil
2006	A*	`conf`	A DC-programming algorithm for kernel selection. ICML Andreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil
2006	—	`conf`	Multi-Task Feature Learning. NIPS Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil
2006	—	`conf`	Prediction on a Graph with a Perceptron. NIPS Mark Herbster, Massimiliano Pontil
2005	—	`conf`	Combining Graph Laplacians for Semi-Supervised Learning. NIPS Andreas Argyriou, Mark Herbster, Massimiliano Pontil
2005	A*	`conf`	Learning Convex Combinations of Continuously Parameterized Basic Kernels. COLT Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil
2005	J	`jnl`	Learning Multiple Tasks with Kernel Methods. J. Mach. Learn. Res. Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil
2005	J	`jnl`	Learning the Kernel Function via Regularization. J. Mach. Learn. Res. Charles A. Micchelli, Massimiliano Pontil
2005	J	`jnl`	On Learning Vector-Valued Functions. Neural Comput. Charles A. Micchelli, Massimiliano Pontil
2005	A*	`conf`	Online learning over graphs. ICML Mark Herbster, Massimiliano Pontil, Lisa Wainer
2005	J	`jnl`	Stability of Randomized Learning Algorithms. J. Mach. Learn. Res. André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil
2005	J	`jnl`	Wide coverage natural language processing using kernel methods and neural networks for structured data. Pattern Recognit. Lett. Sauro Menchetti, Fabrizio Costa, Paolo Frasconi, Massimiliano Pontil
2004	A*	`conf`	A Function Representation for Learning in Banach Spaces. COLT Charles A. Micchelli, Massimiliano Pontil
2004	—	`conf`	Kernels for Multi--task Learning. NIPS Charles A. Micchelli, Massimiliano Pontil
2004	J	`jnl`	Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers. Mach. Learn. Theodoros Evgeniou, Massimiliano Pontil, André Elisseeff
2004	J	`jnl`	New results on error correcting output codes of kernel machines. IEEE Trans. Neural Networks Andrea Passerini, Massimiliano Pontil, Paolo Frasconi
2004	A*	`conf`	Regularized multi--task learning. KDD Theodoros Evgeniou, Massimiliano Pontil
2003	J	`jnl`	A note on different covering numbers in learning theory. J. Complex. Massimiliano Pontil
2003	J	`jnl`	Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines. Pattern Recognit. Yuan Yao, Gian Luca Marcialis, Massimiliano Pontil, Paolo Frasconi, Fabio Roli
2003	J	`jnl`	Full-body person recognition system. Pattern Recognit. Chikahito Nakajima, Massimiliano Pontil, Bernd Heisele, Tomaso A. Poggio
2003	J	`jnl`	Image Representations and Feature Selection for Multimedia Database Search. IEEE Trans. Knowl. Data Eng. Theodoros Evgeniou, Massimiliano Pontil, Constantine Papageorgiou, Tomaso A. Poggio
2003	B	`conf`	On different ensembles of kernel machines. ESANN Michiko Yamana, Hiroyuki Nakahara, Massimiliano Pontil, Shun-ichi Amari
2003	B	`conf`	Reproducing kernels and regularization methods in machine learning. ESANN Massimiliano Pontil
2002	—	`conf`	A Short Review of Statistical Learning Theory. WIRN Massimiliano Pontil
2002	A	`conf`	A Simple Algorithm for Learning Stable Machines. ECAI Savina Andonova, André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil
2002	A	`conf`	From Margins to Probabilities in Multiclass Learning Problems. ECAI Andrea Passerini, Massimiliano Pontil, Paolo Frasconi
2002	—	`conf`	Learning Preference Relations from Data. WIRN Theodoros Evgeniou, Massimiliano Pontil
2002	—	`conf`	Maintenance Training of Electric Power Facilities Using Object Recognition by SVM. SVM Chikahito Nakajima, Massimiliano Pontil
2002	—	`conf`	Support Vector Machines with Clustering for Training with Very Large Datasets. SETN Theodoros Evgeniou, Massimiliano Pontil
2001	—	`conf`	A New Machine Learning Approach to Fingerprint Classification. AI*IA Yuan Yao, Gian Luca Marcialis, Massimiliano Pontil, Paolo Frasconi, Fabio Roli
2001	—	`conf`	Categorization by Learning and Combining Object Parts. NIPS Bernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso A. Poggio
2001	—	`conf`	Component-based Face Detection. CVPR (1) Bernd Heisele, Thomas Serre, Massimiliano Pontil, Tomaso A. Poggio
2001	—	`conf`	Fingerprint Classification with Combinations of Support Vector Machines. AVBPA Yuan Yao, Paolo Frasconi, Massimiliano Pontil
2001	—	`conf`	Support Vector Machines: Theory and Applications. Machine Learning and Its Applications Theodoros Evgeniou, Massimiliano Pontil
2000	B	`conf`	A Note on the Generalization Performance of Kernel Classifiers with Margin. ALT Theodoros Evgeniou, Massimiliano Pontil
2000	A*	`conf`	Bounds on the Generalization Performance of Kernel Machine Ensembles. ICML Theodoros Evgeniou, Luis Pérez-Breva, Massimiliano Pontil, Tomaso A. Poggio
2000	—	`conf`	Feature Selection for SVMs. NIPS Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso A. Poggio, Vladimir Vapnik
2000	B	`conf`	Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation. ICPR Chikahito Nakajima, Norihiko Itoh, Massimiliano Pontil, Tomaso A. Poggio
2000	B	`conf`	On the Noise Model of Support Vector Machines Regression. ALT Massimiliano Pontil, Sayan Mukherjee, Federico Girosi
2000	—	`conf`	People Recognition and Pose Estimation in Image Sequences. IJCNN (4) Chikahito Nakajima, Massimiliano Pontil, Tomaso A. Poggio
2000	J	`jnl`	Regularization Networks and Support Vector Machines. Adv. Comput. Math. Theodoros Evgeniou, Massimiliano Pontil, Tomaso A. Poggio
2000	J	`jnl`	Statistical Learning Theory: A Primer. Int. J. Comput. Vis. Theodoros Evgeniou, Massimiliano Pontil, Tomaso A. Poggio
1999	B	`conf`	A Note on Support Vector Machine Degeneracy. ALT Ryan M. Rifkin, Massimiliano Pontil, Alessandro Verri
1999	B	`conf`	From regression to classification in support vector machines. ESANN Massimiliano Pontil, Ryan M. Rifkin, Theodoros Evgeniou
1999	B	`conf`	On the V ALT Theodoros Evgeniou, Massimiliano Pontil
1999	B	`conf`	Support vector machines vs multi-layer perceptrons in particle identification. ESANN N. Barabino, M. Pallavicini, Alessandro Petrolini, Massimiliano Pontil, Alessandro Verri
1998	J	`jnl`	Properties of Support Vector Machines. Neural Comput. Massimiliano Pontil, Alessandro Verri
1998	—	`conf`	Recognizing 3-D Objects with Linear Support Vector Machines. ECCV (2) Massimiliano Pontil, Stefano Rogai, Alessandro Verri
1998	J	`jnl`	Support Vector Machines for 3D Object Recognition. IEEE Trans. Pattern Anal. Mach. Intell. Massimiliano Pontil, Alessandro Verri
1997	—	`conf`	Direct Aspect-Based 3D Object Recognition. ICIAP (2) Massimiliano Pontil, Alessandro Verri