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2020 – today
- 2022
[j40]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)- [c119]Jordan Frécon, Gilles Gasso, Massimiliano Pontil, Saverio Salzo:
Bregman Neural Networks. ICML 2022: 6779-6792 - [c118]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 - [c117]Dimitri Meunier, Massimiliano Pontil, Carlo Ciliberto:
Distribution Regression with Sliced Wasserstein Kernels. ICML 2022: 15501-15523 - [c116]John Isak Texas Falk, Carlo Ciliberto, Massimiliano Pontil:
Implicit kernel meta-learning using kernel integral forms. UAI 2022: 652-662 - [c115]Rosanna Turrisi, Réemi 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]