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2020 – today
- 2024
[j45]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): 1996-2010 (2024)- [j44]Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil, Saverio Salzo:
High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise]. SIAM J. Math. Data Sci. 6(4): 953-977 (2024) - [j43]Luca Romeo, Temitayo A. Olugbade, Massimiliano Pontil, Nadia Bianchi-Berthouze:
Multi-Rater Consensus Learning for Modeling Multiple Sparse Ratings of Affective Behaviour. IEEE Trans. Affect. Comput. 15(3): 859-871 (2024) - [c131]Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil:
Learning invariant representations of time-homogeneous stochastic dynamical systems. ICLR 2024 - [c130]Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates. ICML 2024 - [c129]Vladimir R. Kostic, Karim Lounici, Prune Inzerilli, Pietro Novelli, Massimiliano Pontil:
Consistent Long-Term Forecasting of Ergodic Dynamical Systems. ICML 2024 - [c128]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: 1318-1329 - [i80]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 abs/2402.15552 (2024) - [i79]Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates. CoRR abs/2403.11687 (2024) - [i78]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 abs/2403.17320 (2024) - [i77]Vladimir R. Kostic, Karim Lounici, Hélène Halconruy, Timothee Devergne, Massimiliano Pontil:
Learning the Infinitesimal Generator of Stochastic Diffusion Processes. CoRR abs/2405.12940 (2024) - [i76]Arya Akhavan, Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov:
Contextual Continuum Bandits: Static Versus Dynamic Regret. CoRR abs/2406.05714 (2024) - [i75]Timothee Devergne, Vladimir Kostic, Michele Parrinello, Massimiliano Pontil:
From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach. CoRR abs/2406.09028 (2024) - [i74]Pietro Novelli, Marco Pratticò, Massimiliano Pontil, Carlo Ciliberto:
Operator World Models for Reinforcement Learning. CoRR abs/2406.19861 (2024) - [i73]Vladimir R. Kostic, Karim Lounici, Grégoire Pacreau, Pietro Novelli, Giacomo Turri, Massimiliano Pontil:
Neural Conditional Probability for Inference. CoRR abs/2407.01171 (2024) - 2023
- [j42]Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. J. Mach. Learn. Res. 24: 167:1-167:37 (2023) - [c127]Leonardo Cella, Karim Lounici, Grégoire Pacreau, Massimiliano Pontil:
Multi-task Representation Learning with Stochastic Linear Bandits. AISTATS 2023: 4822-4847 - [c126]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 - [c125]Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil:
Sharp Spectral Rates for Koopman Operator Learning. NeurIPS 2023 - [c124]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 - [i72]Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil:
Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues. CoRR abs/2302.02004 (2023) - [i71]John Isak Texas Falk, Luigi Bonati, Pietro Novelli, Michele Parrinello, Massimiliano Pontil:
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings. CoRR abs/2306.01589 (2023) - [i70]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 abs/2306.04520 (2023) - [i69]Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil:
Deep projection networks for learning time-homogeneous dynamical systems. CoRR abs/2307.09912 (2023) - [i68]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 abs/2312.07457 (2023) - [i67]Prune Inzerilli, Vladimir Kostic, Karim Lounici, Pietro Novelli, Massimiliano Pontil:
Consistent Long-Term Forecasting of Ergodic Dynamical Systems. CoRR abs/2312.13426 (2023) - [i66]Giacomo Turri, Vladimir Kostic, Pietro Novelli, Massimiliano Pontil:
A randomized algorithm to solve reduced rank operator regression. CoRR abs/2312.17348 (2023) - 2022
- [j41]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): 389-407 (2022) - [j40]Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil:
Multitask Online Mirror Descent. Trans. Mach. Learn. Res. 2022 (2022) - [c123]Jordan Frécon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo:
Bregman Neural Networks. ICML 2022: 6779-6792 - [c122]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: 11529-11558 - [c121]Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto:
Distribution Regression with Sliced Wasserstein Kernels. ICML 2022: 15501-15523 - [c120]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 - [c119]Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto:
Conditional Meta-Learning of Linear Representations. NeurIPS 2022 - [c118]Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil:
Group Meritocratic Fairness in Linear Contextual Bandits. NeurIPS 2022 - [c117]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 - [c116]John Isak Texas Falk, Carlo Ciliberto, Massimiliano Pontil:
Implicit kernel meta-learning using kernel integral forms. UAI 2022: 652-662 - [c115]Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil:
Multi-source domain adaptation via weighted joint distributions optimal transport. UAI 2022: 1970-1980 - [i65]Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-Start. CoRR abs/2202.03397 (2022) - [i64]Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto:
Distribution Regression with Sliced Wasserstein Kernels. CoRR abs/2202.03926 (2022) - [i63]Leonardo Cella, Karim Lounici, Massimiliano Pontil:
Multi-task Representation Learning with Stochastic Linear Bandits. CoRR abs/2202.10066 (2022) - [i62]Pietro Novelli, Luigi Bonati, Massimiliano Pontil, Michele Parrinello:
Characterizing metastable states with the help of machine learning. CoRR abs/2204.07391 (2022) - [i61]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 abs/2205.14027 (2022) - [i60]Leonardo Cella, Karim Lounici, Massimiliano Pontil:
Meta Representation Learning with Contextual Linear Bandits. CoRR abs/2205.15100 (2022) - [i59]Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil:
Group Meritocratic Fairness in Linear Contextual Bandits. CoRR abs/2206.03150 (2022) - [i58]Ruohan Wang, Marco Ciccone, Giulia Luise, Andrew Yapp, Massimiliano Pontil, Carlo Ciliberto:
Schedule-Robust Online Continual Learning. CoRR abs/2210.05561 (2022) - [i57]Ruohan Wang, John Isak Texas Falk, Massimiliano Pontil, Carlo Ciliberto:
Robust Meta-Representation Learning via Global Label Inference and Classification. CoRR abs/2212.11702 (2022) - 2021
- [c114]Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Convergence Properties of Stochastic Hypergradients. AISTATS 2021: 3826-3834 - [c113]Henry Gouk, Timothy M. Hospedales, Massimiliano Pontil:
Distance-Based Regularisation of Deep Networks for Fine-Tuning. ICLR 2021 - [c112]Leonardo Cella, Massimiliano Pontil, Claudio Gentile:
Best Model Identification: A Rested Bandit Formulation. ICML 2021: 1362-1372 - [c111]Andreas Maurer, Daniela Angela Parletta, Andrea Paudice, Massimiliano Pontil:
Robust Unsupervised Learning via L-statistic Minimization. ICML 2021: 7524-7533 - [c110]Mark Herbster, Stephen Pasteris, Fabio Vitale, Massimiliano Pontil:
A Gang of Adversarial Bandits. NeurIPS 2021: 2265-2279 - [c109]Andreas Maurer, Massimiliano Pontil:
Concentration inequalities under sub-Gaussian and sub-exponential conditions. NeurIPS 2021: 7588-7597 - [c108]Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov:
Distributed Zero-Order Optimization under Adversarial Noise. NeurIPS 2021: 10209-10220 - [c107]Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto:
The Role of Global Labels in Few-Shot Classification and How to Infer Them. NeurIPS 2021: 27160-27170 - [c106]Leonardo Cella, Massimiliano Pontil:
Multi-task and meta-learning with sparse linear bandits. UAI 2021: 1692-1702 - [i56]Andreas Maurer, Massimiliano Pontil:
Some Hoeffding- and Bernstein-type Concentration Inequalities. CoRR abs/2102.06304 (2021) - [i55]Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto:
Conditional Meta-Learning of Linear Representations. CoRR abs/2103.16277 (2021) - [i54]Nicolò Cesa-Bianchi, Pierre Laforgue, Andrea Paudice, Massimiliano Pontil:
Multitask Online Mirror Descent. CoRR abs/2106.02393 (2021) - [i53]Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto:
The Role of Global Labels in Few-Shot Classification and How to Infer Them. CoRR abs/2108.04055 (2021) - [i52]Vladimir Kostic, Saverio Salzo, Massimiliano Pontil:
Convergence of Batch Greenkhorn for Regularized Multimarginal Optimal Transport. CoRR abs/2112.00838 (2021) - 2020
- [j39]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: 231-243 (2020) - [j38]Marco Fiorucci, Marina Khoroshiltseva, Massimiliano Pontil, Arianna Traviglia, Alessio Del Bue, Stuart James:
Machine Learning for Cultural Heritage: A Survey. Pattern Recognit. Lett. 133: 102-108 (2020) - [j37]Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini:
Approximating Hamiltonian dynamics with the Nyström method. Quantum 4: 234 (2020) - [c105]Luca Oneto, Michele Donini, Massimiliano Pontil, Andreas Maurer:
Learning Fair and Transferable Representations with Theoretical Guarantees. DSAA 2020: 30-39 - [c104]Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil:
Meta-learning with Stochastic Linear Bandits. ICML 2020: 1360-1370 - [c103]Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo:
On the Iteration Complexity of Hypergradient Computation. ICML 2020: 3748-3758 - [c102]Jordan Frécon, Saverio Salzo, Massimiliano Pontil:
Unveiling Groups of Related Tasks in Multi - Task Learning. ICPR 2020: 7134-7141 - [c101]Michele Donini, Luca Franceschi, Orchid Majumder, Massimiliano Pontil, Paolo Frasconi:
Marthe: Scheduling the Learning Rate Via Online Hypergradients. IJCAI 2020: 2119-2125 - [c100]Luca Oneto, Michele Donini, Massimiliano Pontil:
General Fair Empirical Risk Minimization. IJCNN 2020: 1-8 - [c99]Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov:
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits. NeurIPS 2020 - [c98]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Fair regression with Wasserstein barycenters. NeurIPS 2020 - [c97]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Fair regression via plug-in estimator and recalibration with statistical guarantees. NeurIPS 2020 - [c96]Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto:
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning. NeurIPS 2020 - [c95]Andreas Maurer, Massimiliano Pontil:
Estimating weighted areas under the ROC curve. NeurIPS 2020 - [c94]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 - [c93]Giulia Denevi, Massimiliano Pontil, Dimitrios Stamos:
Online Parameter-Free Learning of Multiple Low Variance Tasks. UAI 2020: 889-898 - [i51]Henry Gouk, Timothy M. Hospedales, Massimiliano Pontil:
Distance-Based Regularisation of Deep Networks for Fine-Tuning. CoRR abs/2002.08253 (2020) - [i50]Feliks Hibraj, Marcello Pelillo, Saverio Salzo, Massimiliano Pontil:
Efficient Tensor Kernel methods for sparse regression. CoRR abs/2003.10482 (2020) - [i49]Leonardo Cella, Alessandro Lazaric, Massimiliano Pontil:
Meta-learning with Stochastic Linear Bandits. CoRR abs/2005.08531 (2020) - [i48]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Fair Regression with Wasserstein Barycenters. CoRR abs/2006.07286 (2020) - [i47]Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov:
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits. CoRR abs/2006.07862 (2020) - [i46]Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil:
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport. CoRR abs/2006.12938 (2020) - [i45]Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo:
On the Iteration Complexity of Hypergradient Computation. CoRR abs/2006.16218 (2020) - [i44]Giulia Denevi, Dimitris Stamos, Massimiliano Pontil:
Online Parameter-Free Learning of Multiple Low Variance Tasks. CoRR abs/2007.05732 (2020) - [i43]Giulia Luise, Massimiliano Pontil, Carlo Ciliberto:
Generalization Properties of Optimal Transport GANs with Latent Distribution Learning. CoRR abs/2007.14641 (2020) - [i42]Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto:
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning. CoRR abs/2008.10857 (2020) - [i41]Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo:
Convergence Properties of Stochastic Hypergradients. CoRR abs/2011.07122 (2020) - [i40]Leonardo Cella, Claudio Gentile, Massimiliano Pontil:
Online Model Selection: a Rested Bandit Formulation. CoRR abs/2012.03522 (2020) - [i39]Andreas Maurer, Daniela A. Parletta, Andrea Paudice, Massimiliano Pontil:
A Perturbation Resilient Framework for Unsupervised Learning. CoRR abs/2012.07399 (2020)
2010 – 2019
- 2019
- [j36]Patrick L. Combettes, Andrew M. McDonald, Charles A. Micchelli, Massimiliano Pontil:
Learning with optimal interpolation norms. Numer. Algorithms 81(2): 695-717 (2019) - [j35]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: 215-231 (2019) - [j34]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): 2140-2147 (2019) - [c92]Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil:
Taking Advantage of Multitask Learning for Fair Classification. AIES 2019: 227-237 - [c91]Andreas Maurer, Massimiliano Pontil:
Uniform concentration and symmetrization for weak interactions. COLT 2019: 2372-2387 - [c90]Luca Oneto, Michele Donini, Massimiliano Pontil:
PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors. ESANN 2019 - [c89]Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil:
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization. ICML 2019: 1566-1575 - [c88]Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He:
Learning Discrete Structures for Graph Neural Networks. ICML 2019: 1972-1982 - [c87]Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto:
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. ICML 2019: 4193-4202 - [c86]Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto:
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. NeurIPS 2019: 9318-9329 - [c85]Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil:
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification. NeurIPS 2019: 12739-12750 - [c84]Giulia Denevi, Dimitris Stamos, Carlo Ciliberto, Massimiliano Pontil:
Online-Within-Online Meta-Learning. NeurIPS 2019: 13089-13099 - [i38]Luca Oneto, Michele Donini, Massimiliano Pontil:
General Fair Empirical Risk Minimization. CoRR abs/1901.10080 (2019) - [i37]Andreas Maurer, Massimiliano Pontil:
Uniform concentration and symmetrization for weak interactions. CoRR abs/1902.01911 (2019) - [i36]Giulia Luise, Dimitris Stamos, Massimiliano Pontil, Carlo Ciliberto:
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. CoRR abs/1903.00667 (2019) - [i35]Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil:
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization. CoRR abs/1903.10399 (2019) - [i34]Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He:
Learning Discrete Structures for Graph Neural Networks. CoRR abs/1903.11960 (2019) - [i33]Giulia Luise, Saverio Salzo, Massimiliano Pontil, Carlo Ciliberto:
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm. CoRR abs/1905.13194 (2019) - [i32]Luca Oneto, Michele Donini, Andreas Maurer, Massimiliano Pontil:
Learning Fair and Transferable Representations. CoRR abs/1906.10673 (2019) - [i31]Michele Donini, Luca Franceschi, Massimiliano Pontil, Orchid Majumder, Paolo Frasconi:
Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm. CoRR abs/1910.08525 (2019) - 2018
- [j33]Peixi Peng, Yonghong Tian, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Tiejun Huang:
Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning. IEEE Trans. Pattern Anal. Mach. Intell. 40(7): 1625-1638 (2018) - [j32]Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil:
Learning local metrics from pairwise similarity data. Pattern Recognit. 75: 315-326 (2018) - [c83]Andreas Maurer, Massimiliano Pontil:
Empirical bounds for functions with weak interactions. COLT 2018: 987-1010 - [c82]Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimiliano Pontil:
Bilevel Programming for Hyperparameter Optimization and Meta-Learning. ICML 2018: 1563-1572 - [c81]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization Under Fairness Constraints. NeurIPS 2018: 2796-2806 - [c80]Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto:
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. NeurIPS 2018: 5864-5874 - [c79]Jordan Frécon, Saverio Salzo, Massimiliano Pontil:
Bilevel learning of the Group Lasso structure. NeurIPS 2018: 8311-8321 - [c78]Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil:
Learning To Learn Around A Common Mean. NeurIPS 2018: 10190-10200 - [c77]Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil:
Incremental Learning-to-Learn with Statistical Guarantees. UAI 2018: 457-466 - [i30]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization under Fairness Constraints. CoRR abs/1802.08626 (2018) - [i29]Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil:
Incremental Learning-to-Learn with Statistical Guarantees. CoRR abs/1803.08089 (2018) - [i28]Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto, Andrea Rocchetto, Massimiliano Pontil, Simone Severini:
Approximating Hamiltonian dynamics with the Nyström method. CoRR abs/1804.02484 (2018) - [i27]Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto:
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance. CoRR abs/1805.11897 (2018) - [i26]Luca Franceschi, Paolo Frasconi, Saverio Salzo, Massimiliano Pontil:
Bilevel Programming for Hyperparameter Optimization and Meta-Learning. CoRR abs/1806.04910 (2018) - [i25]Luca Franceschi, Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo, Paolo Frasconi:
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning. CoRR abs/1806.04941 (2018) - [i24]Octavio Antonio Villarreal-Magaña, Victor Barasuol, Marco Camurri, Michele Focchi, Luca Franceschi, Massimiliano Pontil, Darwin G. Caldwell, Claudio Semini:
Fast and Continuous Foothold Adaptation for Dynamic Locomotion through Convolutional Neural Networks. CoRR abs/1809.09759 (2018) - [i23]Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil:
Taking Advantage of Multitask Learning for Fair Classification. CoRR abs/1810.08683 (2018) - 2017
- [c76]Pierre Alquier, The Tien Mai, Massimiliano Pontil:
Regret Bounds for Lifelong Learning. AISTATS 2017: 261-269 - [c75]Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil:
On Hyperparameter Optimization in Learning Systems. ICLR (Workshop) 2017 - [c74]Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil:
Forward and Reverse Gradient-Based Hyperparameter Optimization. ICML 2017: 1165-1173 - [c73]Leonardo Badino, Luca Franceschi, Raman Arora, Michele Donini, Massimiliano Pontil:
A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion. INTERSPEECH 2017: 984-988 - [c72]Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil:
Consistent Multitask Learning with Nonlinear Output Relations. NIPS 2017: 1986-1996 - [i22]Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil:
Consistent Multitask Learning with Nonlinear Output Relations. CoRR abs/1705.08118 (2017) - [i21]Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil:
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization. CoRR abs/1706.08934 (2017) - [i20]Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, Leonard Wossnig:
Quantum machine learning: a classical perspective. CoRR abs/1707.08561 (2017) - [i19]Luca Franceschi, Michele Donini, Paolo Frasconi, Massimiliano Pontil:
A Bridge Between Hyperparameter Optimization and Larning-to-learn. CoRR abs/1712.06283 (2017) - 2016
- [j31]Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
The Benefit of Multitask Representation Learning. J. Mach. Learn. Res. 17: 81:1-81:32 (2016) - [j30]Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos:
New Perspectives on k-Support and Cluster Norms. J. Mach. Learn. Res. 17: 155:1-155:38 (2016) - [c71]Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos:
Fitting Spectral Decay with the k-Support Norm. AISTATS 2016: 1061-1069 - [c70]Peixi Peng, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian:
Unsupervised Cross-Dataset Transfer Learning for Person Re-identification. CVPR 2016: 1306-1315 - [c69]Julien Bohné, Sylvain Colin, Stéphane Gentric, Massimiliano Pontil:
Similarity Function Learning with Data Uncertainty. ICPRAM 2016: 131-140 - [c68]Michele Donini, David Martínez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil:
Distributed variance regularized Multitask Learning. IJCNN 2016: 3101-3109 - [c67]Michele Donini, João M. Monteiro, Massimiliano Pontil, John Shawe-Taylor, Janaina Mourão Miranda:
A multimodal multiple kernel learning approach to Alzheimer's disease detection. MLSP 2016: 1-6 - [c66]Mark Herbster, Stephen Pasteris, Massimiliano Pontil:
Mistake Bounds for Binary Matrix Completion. NIPS 2016: 3954-3962 - [i18]Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos:
Fitting Spectral Decay with the $k$-Support Norm. CoRR abs/1601.00449 (2016) - [i17]Andreas Maurer, Massimiliano Pontil:
Bounds for Vector-Valued Function Estimation. CoRR abs/1606.01487 (2016) - [i16]Pierre Alquier, The Tien Mai, Massimiliano Pontil:
Regret Bounds for Lifelong Learning. CoRR abs/1610.08628 (2016) - 2015
- [j29]Mark Herbster, Stephen Pasteris, Massimiliano Pontil:
Predicting a switching sequence of graph labelings. J. Mach. Learn. Res. 16: 2003-2022 (2015) - [c65]Dimitris Stamos, Samuele Martelli, Moin Nabi, Andrew M. McDonald, Vittorio Murino, Massimiliano Pontil:
Learning with dataset bias in latent subcategory models. CVPR 2015: 3650-3658 - [i15]Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
The Benefit of Multitask Representation Learning. CoRR abs/1505.06279 (2015) - [i14]Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos:
New Perspectives on $k$-Support and Cluster Norms. CoRR abs/1512.08204 (2015) - [i13]Trevor Darrell, Marius Kloft, Massimiliano Pontil, Gunnar Rätsch, Erik Rodner:
Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). Dagstuhl Reports 5(4): 18-55 (2015) - 2014
- [j28]Jair Montoya-Martínez, Antonio Artés-Rodríguez, Massimiliano Pontil, Lars Kai Hansen:
A regularized matrix factorization approach to induce structured sparse-low-rank solutions in the EEG inverse problem. EURASIP J. Adv. Signal Process. 2014: 97 (2014) - [c64]Jair Montoya-Martínez, Antonio Artés-Rodríguez, Massimiliano Pontil:
Structured sparse-low rank matrix factorization for the EEG inverse problem. CIP 2014: 1-6 - [c63]Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning. COLT 2014: 440-460 - [c62]Julien Bohné, Yiming Ying, Stéphane Gentric, Massimiliano Pontil:
Large Margin Local Metric Learning. ECCV (2) 2014: 679-694 - [c61]Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos:
Spectral k-Support Norm Regularization. NIPS 2014: 3644-3652 - [i12]Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning. CoRR abs/1402.1864 (2014) - 2013
- [j27]Charles A. Micchelli, Jean Morales, Massimiliano Pontil:
Regularizers for structured sparsity. Adv. Comput. Math. 38(3): 455-489 (2013) - [c60]Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil:
On Sparsity Inducing Regularization Methods for Machine Learning. Empirical Inference 2013: 205-216 - [c59]Massimiliano Pontil, Andreas Maurer:
Excess risk bounds for multitask learning with trace norm regularization. COLT 2013: 55-76 - [c58]Bernardino Romera-Paredes, Min S. H. Aung, Massimiliano Pontil, Nadia Bianchi-Berthouze, Amanda C. de C. Williams, Paul J. Watson:
Transfer learning to account for idiosyncrasy in face and body expressions. FG 2013: 1-6 - [c57]Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
Sparse coding for multitask and transfer learning. ICML (2) 2013: 343-351 - [c56]Bernardino Romera-Paredes, Hane Aung, Nadia Bianchi-Berthouze, Massimiliano Pontil:
Multilinear Multitask Learning. ICML (3) 2013: 1444-1452 - [c55]Bernardino Romera-Paredes, Massimiliano Pontil:
A New Convex Relaxation for Tensor Completion. NIPS 2013: 2967-2975 - [c54]David Martínez-Rego, Massimiliano Pontil:
Multi-task Averaging via Task Clustering. SIMBAD 2013: 148-159 - [i11]Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil:
On Sparsity Inducing Regularization Methods for Machine Learning. CoRR abs/1303.6086 (2013) - [i10]Bernardino Romera-Paredes, Massimiliano Pontil:
A New Convex Relaxation for Tensor Completion. CoRR abs/1307.4653 (2013) - 2012
- [j26]David T. Jones, Daniel W. A. Buchan, Domenico Cozzetto, Massimiliano Pontil:
PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinform. 28(2): 184-190 (2012) - [j25]Andreas Maurer, Massimiliano Pontil:
Structured Sparsity and Generalization. J. Mach. Learn. Res. 13: 671-690 (2012) - [c53]Andreas Maurer, Massimiliano Pontil:
Transfer learning in a heterogeneous environment. CIP 2012: 1-6 - [c52]Jair Montoya-Martínez, Antonio Artés-Rodríguez, Lars Kai Hansen, Massimiliano Pontil:
Structured sparsity regularization approach to the EEG inverse problem. CIP 2012: 1-6 - [c51]Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton:
Modelling transition dynamics in MDPs with RKHS embeddings. ICML 2012 - [c50]Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil:
Conditional mean embeddings as regressors. ICML 2012 - [c49]Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu:
Optimal kernel choice for large-scale two-sample tests. NIPS 2012: 1214-1222 - [c48]Luca Baldassarre, Janaina Mourão Miranda, Massimiliano Pontil:
Structured Sparsity Models for Brain Decoding from fMRI Data. PRNI 2012: 5-8 - [c47]Luca Baldassarre, Jean Morales, Andreas Argyriou, Massimiliano Pontil:
A General Framework for Structured Sparsity via Proximal Optimization. AISTATS 2012: 82-90 - [c46]Bernardino Romera-Paredes, Andreas Argyriou, Nadia Berthouze, Massimiliano Pontil:
Exploiting Unrelated Tasks in Multi-Task Learning. AISTATS 2012: 951-959 - [i9]Steffen Grünewälder, Guy Lever, Luca Baldassarre, Sam Patterson, Arthur Gretton, Massimiliano Pontil:
Conditional mean embeddings as regressors - supplementary. CoRR abs/1205.4656 (2012) - [i8]Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton:
Modelling transition dynamics in MDPs with RKHS embeddings. CoRR abs/1206.4655 (2012) - [i7]Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes:
Sparse coding for multitask and transfer learning. CoRR abs/1209.0738 (2012) - [i6]Andreas Maurer, Massimiliano Pontil:
Excess risk bounds for multitask learning with trace norm regularization. CoRR abs/1212.1496 (2012) - 2011
- [c45]Anastasios Noulas, Salvatore Scellato, Cecilia Mascolo, Massimiliano Pontil:
An Empirical Study of Geographic User Activity Patterns in Foursquare. ICWSM 2011 - [c44]Anastasios Noulas, Salvatore Scellato, Cecilia Mascolo, Massimiliano Pontil:
Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks. The Social Mobile Web 2011 - [i5]Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Lixin Shen, Yuesheng Xu:
Efficient First Order Methods for Linear Composite Regularizers. CoRR abs/1104.1436 (2011) - [i4]Andreas Argyriou, Luca Baldassarre, Jean Morales, Massimiliano Pontil:
A General Framework for Structured Sparsity via Proximal Optimization. CoRR abs/1106.5236 (2011) - [i3]Andreas Maurer, Massimiliano Pontil:
Structured Sparsity and Generalization. CoRR abs/1108.3476 (2011) - [i2]Anastasios Noulas, Salvatore Scellato, Renaud Lambiotte, Massimiliano Pontil, Cecilia Mascolo:
A tale of many cities: universal patterns in human urban mobility. CoRR abs/1108.5355 (2011) - 2010
- [j24]Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil:
On Spectral Learning. J. Mach. Learn. Res. 11: 935-953 (2010) - [j23]Augusto Maurer, Massimiliano Pontil:
K -Dimensional Coding Schemes in Hilbert Spaces. IEEE Trans. Inf. Theory 56(11): 5839-5846 (2010) - [c43]Charles A. Micchelli, Jean Morales, Massimiliano Pontil:
A Family of Penalty Functions for Structured Sparsity. NIPS 2010: 1612-1623
2000 – 2009
- 2009
- [j22]Andrea Caponnetto, Ernesto De Vito, Massimiliano Pontil:
Entropy conditions for L r -convergence of empirical processes. Adv. Comput. Math. 30(4): 355-373 (2009) - [j21]Stefano Lise, Cédric Archambeau, Massimiliano Pontil, David T. Jones:
Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods. BMC Bioinform. 10: 365 (2009) - [j20]Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil:
When Is There a Representer Theorem? Vector Versus Matrix Regularizers. J. Mach. Learn. Res. 10: 2507-2529 (2009) - [c42]Karim Lounici, Massimiliano Pontil, Alexandre B. Tsybakov, Sara A. van de Geer:
Taking Advantage of Sparsity in Multi-Task Learning. COLT 2009 - [c41]Andreas Maurer, Massimiliano Pontil:
Empirical Bernstein Bounds and Sample-Variance Penalization. COLT 2009 - [p1]Francesca Odone, Massimiliano Pontil, Alessandro Verri:
Machine Learning Techniques for Biometrics. Handbook of Remote Biometrics 2009: 247-271 - 2008
- [j19]Yiming Ying, Massimiliano Pontil:
Online Gradient Descent Learning Algorithms. Found. Comput. Math. 8(5): 561-596 (2008) - [j18]Andrea Caponnetto, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying:
Universal Multi-Task Kernels. J. Mach. Learn. Res. 9: 1615-1646 (2008) - [j17]Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil:
Convex multi-task feature learning. Mach. Learn. 73(3): 243-272 (2008) - [c40]Andreas Maurer, Massimiliano Pontil:
A Uniform Lower Error Bound for Half-Space Learning. ALT 2008: 70-78 - [c39]Andreas Maurer, Massimiliano Pontil:
Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces. ALT 2008: 79-91 - [c38]Mark Herbster, Guy Lever, Massimiliano Pontil:
Online Prediction on Large Diameter Graphs. NIPS 2008: 649-656 - [c37]Mark Herbster, Massimiliano Pontil, Sergio Rojas Galeano:
Fast Prediction on a Tree. NIPS 2008: 657-664 - [c36]Andreas Argyriou, Andreas Maurer, Massimiliano Pontil:
An Algorithm for Transfer Learning in a Heterogeneous Environment. ECML/PKDD (1) 2008: 71-85 - [i1]Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil:
When is there a representer theorem? Vector versus matrix regularizers. CoRR abs/0809.1590 (2008) - 2007
- [j16]Charles A. Micchelli, Massimiliano Pontil:
Feature space perspectives for learning the kernel. Mach. Learn. 66(2-3): 297-319 (2007) - [c35]Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying:
A Spectral Regularization Framework for Multi-Task Structure Learning. NIPS 2007: 25-32 - 2006
- [c34]Andreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil:
A DC-programming algorithm for kernel selection. ICML 2006: 41-48 - [c33]Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil:
Multi-Task Feature Learning. NIPS 2006: 41-48 - [c32]Mark Herbster, Massimiliano Pontil:
Prediction on a Graph with a Perceptron. NIPS 2006: 577-584 - 2005
- [j15]André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil:
Stability of Randomized Learning Algorithms. J. Mach. Learn. Res. 6: 55-79 (2005) - [j14]Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil:
Learning Multiple Tasks with Kernel Methods. J. Mach. Learn. Res. 6: 615-637 (2005) - [j13]Charles A. Micchelli, Massimiliano Pontil:
Learning the Kernel Function via Regularization. J. Mach. Learn. Res. 6: 1099-1125 (2005) - [j12]Charles A. Micchelli, Massimiliano Pontil:
On Learning Vector-Valued Functions. Neural Comput. 17(1): 177-204 (2005) - [j11]Sauro Menchetti, Fabrizio Costa, Paolo Frasconi, Massimiliano Pontil:
Wide coverage natural language processing using kernel methods and neural networks for structured data. Pattern Recognit. Lett. 26(12): 1896-1906 (2005) - [c31]Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil:
Learning Convex Combinations of Continuously Parameterized Basic Kernels. COLT 2005: 338-352 - [c30]Mark Herbster, Massimiliano Pontil, Lisa Wainer:
Online learning over graphs. ICML 2005: 305-312 - [c29]Andreas Argyriou, Mark Herbster, Massimiliano Pontil:
Combining Graph Laplacians for Semi-Supervised Learning. NIPS 2005: 67-74 - 2004
- [j10]Theodoros Evgeniou, Massimiliano Pontil, André Elisseeff:
Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers. Mach. Learn. 55(1): 71-97 (2004) - [j9]Andrea Passerini, Massimiliano Pontil, Paolo Frasconi:
New results on error correcting output codes of kernel machines. IEEE Trans. Neural Networks 15(1): 45-54 (2004) - [c28]Charles A. Micchelli, Massimiliano Pontil:
A Function Representation for Learning in Banach Spaces. COLT 2004: 255-269 - [c27]Theodoros Evgeniou, Massimiliano Pontil:
Regularized multi--task learning. KDD 2004: 109-117 - [c26]Charles A. Micchelli, Massimiliano Pontil:
Kernels for Multi--task Learning. NIPS 2004: 921-928 - 2003
- [j8]Massimiliano Pontil:
A note on different covering numbers in learning theory. J. Complex. 19(5): 665-671 (2003) - [j7]Yuan Yao, Gian Luca Marcialis, Massimiliano Pontil, Paolo Frasconi, Fabio Roli:
Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines. Pattern Recognit. 36(2): 397-406 (2003) - [j6]Chikahito Nakajima, Massimiliano Pontil, Bernd Heisele, Tomaso A. Poggio:
Full-body person recognition system. Pattern Recognit. 36(9): 1997-2006 (2003) - [j5]Theodoros Evgeniou, Massimiliano Pontil, Constantine Papageorgiou, Tomaso A. Poggio:
Image Representations and Feature Selection for Multimedia Database Search. IEEE Trans. Knowl. Data Eng. 15(4): 911-920 (2003) - [c25]Massimiliano Pontil:
Reproducing kernels and regularization methods in machine learning. ESANN 2003: 185-196 - [c24]Michiko Yamana, Hiroyuki Nakahara, Massimiliano Pontil, Shun-ichi Amari:
On different ensembles of kernel machines. ESANN 2003: 197-201 - 2002
- [c23]Andrea Passerini, Massimiliano Pontil, Paolo Frasconi:
From Margins to Probabilities in Multiclass Learning Problems. ECAI 2002: 400-404 - [c22]Savina Andonova, André Elisseeff, Theodoros Evgeniou, Massimiliano Pontil:
A Simple Algorithm for Learning Stable Machines. ECAI 2002: 513-517 - [c21]Theodoros Evgeniou, Massimiliano Pontil:
Support Vector Machines with Clustering for Training with Very Large Datasets. SETN 2002: 346-354 - [c20]Chikahito Nakajima, Massimiliano Pontil:
Maintenance Training of Electric Power Facilities Using Object Recognition by SVM. SVM 2002: 112-119 - [c19]Theodoros Evgeniou, Massimiliano Pontil:
Learning Preference Relations from Data. WIRN 2002: 23-34 - [c18]Massimiliano Pontil:
A Short Review of Statistical Learning Theory. WIRN 2002: 233-242 - 2001
- [c17]Theodoros Evgeniou, Massimiliano Pontil:
Support Vector Machines: Theory and Applications. Machine Learning and Its Applications 2001: 249-257 - [c16]Yuan Yao, Gian Luca Marcialis, Massimiliano Pontil, Paolo Frasconi, Fabio Roli:
A New Machine Learning Approach to Fingerprint Classification. AI*IA 2001: 57-63 - [c15]Yuan Yao, Paolo Frasconi, Massimiliano Pontil:
Fingerprint Classification with Combinations of Support Vector Machines. AVBPA 2001: 253-258 - [c14]Bernd Heisele, Thomas Serre, Massimiliano Pontil, Tomaso A. Poggio:
Component-based Face Detection. CVPR (1) 2001: 657-662 - [c13]Bernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso A. Poggio:
Categorization by Learning and Combining Object Parts. NIPS 2001: 1239-1245 - 2000
- [j4]Theodoros Evgeniou, Massimiliano Pontil, Tomaso A. Poggio:
Regularization Networks and Support Vector Machines. Adv. Comput. Math. 13(1): 1-50 (2000) - [j3]Theodoros Evgeniou, Massimiliano Pontil, Tomaso A. Poggio:
Statistical Learning Theory: A Primer. Int. J. Comput. Vis. 38(1): 9-13 (2000) - [c12]Theodoros Evgeniou, Massimiliano Pontil:
A Note on the Generalization Performance of Kernel Classifiers with Margin. ALT 2000: 306-315 - [c11]Massimiliano Pontil, Sayan Mukherjee, Federico Girosi:
On the Noise Model of Support Vector Machines Regression. ALT 2000: 316-324 - [c10]Theodoros Evgeniou, Luis Pérez-Breva, Massimiliano Pontil, Tomaso A. Poggio:
Bounds on the Generalization Performance of Kernel Machine Ensembles. ICML 2000: 271-278 - [c9]Chikahito Nakajima, Norihiko Itoh, Massimiliano Pontil, Tomaso A. Poggio:
Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation. ICPR 2000: 4787-4790 - [c8]Chikahito Nakajima, Massimiliano Pontil, Tomaso A. Poggio:
People Recognition and Pose Estimation in Image Sequences. IJCNN (4) 2000: 189-196 - [c7]Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso A. Poggio, Vladimir Vapnik:
Feature Selection for SVMs. NIPS 2000: 668-674
1990 – 1999
- 1999
- [c6]Theodoros Evgeniou, Massimiliano Pontil:
On the Vgamma Dimension for Regression in Reproducing Kernel Hilbert Spaces. ALT 1999: 106-117 - [c5]Ryan M. Rifkin, Massimiliano Pontil, Alessandro Verri:
A Note on Support Vector Machine Degeneracy. ALT 1999: 252-263 - [c4]Massimiliano Pontil, Ryan M. Rifkin, Theodoros Evgeniou:
From regression to classification in support vector machines. ESANN 1999: 225-230 - [c3]N. Barabino, M. Pallavicini, Alessandro Petrolini, Massimiliano Pontil, Alessandro Verri:
Support vector machines vs multi-layer perceptrons in particle identification. ESANN 1999: 257-262 - 1998
- [j2]Massimiliano Pontil, Alessandro Verri:
Properties of Support Vector Machines. Neural Comput. 10(4): 955-974 (1998) - [j1]Massimiliano Pontil, Alessandro Verri:
Support Vector Machines for 3D Object Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 20(6): 637-646 (1998) - [c2]Massimiliano Pontil, Stefano Rogai, Alessandro Verri:
Recognizing 3-D Objects with Linear Support Vector Machines. ECCV (2) 1998: 469-483 - 1997
- [c1]Massimiliano Pontil, Alessandro Verri:
Direct Aspect-Based 3D Object Recognition. ICIAP (2) 1997: 300-307
Coauthor Index
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