Massimiliano Pontil

According to our database1, Massimiliano Pontil authored at least 157 papers between 1997 and 2020.

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Bibliography

2020
Machine Learning for Cultural Heritage: A Survey.
Pattern Recognit. Lett., 2020

Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy.
Neurocomputing, 2020

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning.
CoRR, 2020

Generalization Properties of Optimal Transport GANs with Latent Distribution Learning.
CoRR, 2020

On the Iteration Complexity of Hypergradient Computation.
CoRR, 2020

Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport.
CoRR, 2020

Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits.
CoRR, 2020

Fair Regression with Wasserstein Barycenters.
CoRR, 2020

Meta-learning with Stochastic Linear Bandits.
CoRR, 2020

Efficient Tensor Kernel methods for sparse regression.
CoRR, 2020

Distance-Based Regularisation of Deep Networks for Fine-Tuning.
CoRR, 2020

Online Parameter-Free Learning of Multiple Low Variance Tasks.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

General Fair Empirical Risk Minimization.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Marthe: Scheduling the Learning Rate Via Online Hypergradients.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

2019
Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs.
IEEE Robotics Autom. Lett., 2019

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.
NeuroImage, 2019

Learning with optimal interpolation norms.
Numer. Algorithms, 2019

Scheduling the Learning Rate via Hypergradients: New Insights and a New Algorithm.
CoRR, 2019

Learning Fair and Transferable Representations.
CoRR, 2019

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Online-Within-Online Meta-Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction.
Proceedings of the 36th International Conference on Machine Learning, 2019

Learning Discrete Structures for Graph Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization.
Proceedings of the 36th International Conference on Machine Learning, 2019

PAC-Bayes and Fairness: Risk and Fairness Bounds on Distribution Dependent Fair Priors.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Uniform concentration and symmetrization for weak interactions.
Proceedings of the Conference on Learning Theory, 2019

Taking Advantage of Multitask Learning for Fair Classification.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018
Learning local metrics from pairwise similarity data.
Pattern Recognit., 2018

Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2018

Fast and Continuous Foothold Adaptation for Dynamic Locomotion through Convolutional Neural Networks.
CoRR, 2018

Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning.
CoRR, 2018

Bilevel Programming for Hyperparameter Optimization and Meta-Learning.
CoRR, 2018

Approximating Hamiltonian dynamics with the Nyström method.
CoRR, 2018

Incremental Learning-to-Learn with Statistical Guarantees.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Bilevel learning of the Group Lasso structure.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Empirical Risk Minimization Under Fairness Constraints.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning To Learn Around A Common Mean.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Bilevel Programming for Hyperparameter Optimization and Meta-Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Empirical bounds for functions with weak interactions.
Proceedings of the Conference On Learning Theory, 2018

2017
A Bridge Between Hyperparameter Optimization and Larning-to-learn.
CoRR, 2017

Reexamining Low Rank Matrix Factorization for Trace Norm Regularization.
CoRR, 2017

Quantum machine learning: a classical perspective.
CoRR, 2017

Consistent Multitask Learning with Nonlinear Output Relations.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A Speaker Adaptive DNN Training Approach for Speaker-Independent Acoustic Inversion.
Proceedings of the Interspeech 2017, 2017

Forward and Reverse Gradient-Based Hyperparameter Optimization.
Proceedings of the 34th International Conference on Machine Learning, 2017

On Hyperparameter Optimization in Learning Systems.
Proceedings of the 5th International Conference on Learning Representations, 2017

Regret Bounds for Lifelong Learning.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
New Perspectives on k-Support and Cluster Norms.
J. Mach. Learn. Res., 2016

The Benefit of Multitask Representation Learning.
J. Mach. Learn. Res., 2016

Bounds for Vector-Valued Function Estimation.
CoRR, 2016

Mistake Bounds for Binary Matrix Completion.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A multimodal multiple kernel learning approach to Alzheimer's disease detection.
Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

Distributed variance regularized Multitask Learning.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Similarity Function Learning with Data Uncertainty.
Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, 2016

Unsupervised Cross-Dataset Transfer Learning for Person Re-identification.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Fitting Spectral Decay with the k-Support Norm.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Predicting a switching sequence of graph labelings.
J. Mach. Learn. Res., 2015

Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152).
Dagstuhl Reports, 2015

Learning with dataset bias in latent subcategory models.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014
A regularized matrix factorization approach to induce structured sparse-low-rank solutions in the EEG inverse problem.
EURASIP J. Adv. Signal Process., 2014

Spectral k-Support Norm Regularization.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Large Margin Local Metric Learning.
Proceedings of the Computer Vision - ECCV 2014, 2014

An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning.
Proceedings of The 27th Conference on Learning Theory, 2014

Structured sparse-low rank matrix factorization for the EEG inverse problem.
Proceedings of the 4th International Workshop on Cognitive Information Processing, 2014

2013
Regularizers for structured sparsity.
Adv. Comput. Math., 2013

Multi-task Averaging via Task Clustering.
Proceedings of the Similarity-Based Pattern Recognition - Second International Workshop, 2013

A New Convex Relaxation for Tensor Completion.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Multilinear Multitask Learning.
Proceedings of the 30th International Conference on Machine Learning, 2013

Sparse coding for multitask and transfer learning.
Proceedings of the 30th International Conference on Machine Learning, 2013

Transfer learning to account for idiosyncrasy in face and body expressions.
Proceedings of the 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, 2013

Excess risk bounds for multitask learning with trace norm regularization.
Proceedings of the COLT 2013, 2013

On Sparsity Inducing Regularization Methods for Machine Learning.
Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2012
Exploiting Unrelated Tasks in Multi-Task Learning.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Structured Sparsity and Generalization.
J. Mach. Learn. Res., 2012

A General Framework for Structured Sparsity via Proximal Optimization.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Conditional mean embeddings as regressors - supplementary
CoRR, 2012

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments.
Bioinform., 2012

Structured Sparsity Models for Brain Decoding from fMRI Data.
Proceedings of the Second International Workshop on Pattern Recognition in NeuroImaging, 2012

Optimal kernel choice for large-scale two-sample tests.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Conditional mean embeddings as regressors.
Proceedings of the 29th International Conference on Machine Learning, 2012

Modelling transition dynamics in MDPs with RKHS embeddings.
Proceedings of the 29th International Conference on Machine Learning, 2012

Structured sparsity regularization approach to the EEG inverse problem.
Proceedings of the 3rd International Workshop on Cognitive Information Processing, 2012

Transfer learning in a heterogeneous environment.
Proceedings of the 3rd International Workshop on Cognitive Information Processing, 2012

2011
A tale of many cities: universal patterns in human urban mobility
CoRR, 2011

Efficient First Order Methods for Linear Composite Regularizers
CoRR, 2011

Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks.
Proceedings of the Social Mobile Web, 2011

An Empirical Study of Geographic User Activity Patterns in Foursquare.
Proceedings of the Fifth International Conference on Weblogs and Social Media, 2011

2010
K -Dimensional Coding Schemes in Hilbert Spaces.
IEEE Trans. Inf. Theory, 2010

On Spectral Learning.
J. Mach. Learn. Res., 2010

A Family of Penalty Functions for Structured Sparsity.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009
When Is There a Representer Theorem? Vector Versus Matrix Regularizers.
J. Mach. Learn. Res., 2009

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods.
BMC Bioinform., 2009

Entropy conditions for <i>L</i> <sub> <i>r</i> </sub>-convergence of empirical processes.
Adv. Comput. Math., 2009

Empirical Bernstein Bounds and Sample-Variance Penalization.
Proceedings of the COLT 2009, 2009

Taking Advantage of Sparsity in Multi-Task Learning.
Proceedings of the COLT 2009, 2009

Machine Learning Techniques for Biometrics.
Proceedings of the Handbook of Remote Biometrics, 2009

2008
Convex multi-task feature learning.
Mach. Learn., 2008

Universal Multi-Task Kernels.
J. Mach. Learn. Res., 2008

Online Gradient Descent Learning Algorithms.
Found. Comput. Math., 2008

An Algorithm for Transfer Learning in a Heterogeneous Environment.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Fast Prediction on a Tree.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Online Prediction on Large Diameter Graphs.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces.
Proceedings of the Algorithmic Learning Theory, 19th International Conference, 2008

A Uniform Lower Error Bound for Half-Space Learning.
Proceedings of the Algorithmic Learning Theory, 19th International Conference, 2008

2007
Feature space perspectives for learning the kernel.
Mach. Learn., 2007

A Spectral Regularization Framework for Multi-Task Structure Learning.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006
Prediction on a Graph with a Perceptron.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Multi-Task Feature Learning.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

A DC-programming algorithm for kernel selection.
Proceedings of the Machine Learning, 2006

2005
Wide coverage natural language processing using kernel methods and neural networks for structured data.
Pattern Recognit. Lett., 2005

On Learning Vector-Valued Functions.
Neural Comput., 2005

Learning the Kernel Function via Regularization.
J. Mach. Learn. Res., 2005

Learning Multiple Tasks with Kernel Methods.
J. Mach. Learn. Res., 2005

Stability of Randomized Learning Algorithms.
J. Mach. Learn. Res., 2005

Combining Graph Laplacians for Semi-Supervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Online learning over graphs.
Proceedings of the Machine Learning, 2005

Learning Convex Combinations of Continuously Parameterized Basic Kernels.
Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005

2004
New results on error correcting output codes of kernel machines.
IEEE Trans. Neural Networks, 2004

Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers.
Mach. Learn., 2004

Kernels for Multi--task Learning.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Regularized multi--task learning.
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

A Function Representation for Learning in Banach Spaces.
Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004

2003
Image Representations and Feature Selection for Multimedia Database Search.
IEEE Trans. Knowl. Data Eng., 2003

Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines.
Pattern Recognit., 2003

Full-body person recognition system.
Pattern Recognit., 2003

A note on different covering numbers in learning theory.
J. Complex., 2003

On different ensembles of kernel machines.
Proceedings of the ESANN 2003, 2003

Reproducing kernels and regularization methods in machine learning.
Proceedings of the ESANN 2003, 2003

2002
A Short Review of Statistical Learning Theory.
Proceedings of the Neural Nets, 13th Italian Workshop on Neural Nets, 2002

Learning Preference Relations from Data.
Proceedings of the Neural Nets, 13th Italian Workshop on Neural Nets, 2002

Maintenance Training of Electric Power Facilities Using Object Recognition by SVM.
Proceedings of the Pattern Recognition with Support Vector Machines, 2002

Support Vector Machines with Clustering for Training with Very Large Datasets.
Proceedings of the Methods and Applications of Artificial Intelligence, 2002

From Margins to Probabilities in Multiclass Learning Problems.
Proceedings of the 15th Eureopean Conference on Artificial Intelligence, 2002

A Simple Algorithm for Learning Stable Machines.
Proceedings of the 15th Eureopean Conference on Artificial Intelligence, 2002

2001
Categorization by Learning and Combining Object Parts.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Component-based Face Detection.
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 2001

Fingerprint Classification with Combinations of Support Vector Machines.
Proceedings of the Audio- and Video-Based Biometric Person Authentication, 2001

A New Machine Learning Approach to Fingerprint Classification.
Proceedings of the AI*IA 2001: Advances in Artificial Intelligence, 2001

Support Vector Machines: Theory and Applications.
Proceedings of the Machine Learning and Its Applications, Advanced Lectures, 2001

2000
Statistical Learning Theory: A Primer.
Int. J. Comput. Vis., 2000

Regularization Networks and Support Vector Machines.
Adv. Comput. Math., 2000

Feature Selection for SVMs.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

People Recognition and Pose Estimation in Image Sequences.
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Object Recognition and Detection by a Combination of Support Vector Machine and Rotation Invariant Phase Only Correlation.
Proceedings of the 15th International Conference on Pattern Recognition, 2000

Bounds on the Generalization Performance of Kernel Machine Ensembles.
Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

On the Noise Model of Support Vector Machines Regression.
Proceedings of the Algorithmic Learning Theory, 11th International Conference, 2000

A Note on the Generalization Performance of Kernel Classifiers with Margin.
Proceedings of the Algorithmic Learning Theory, 11th International Conference, 2000

1999
From regression to classification in support vector machines.
Proceedings of the ESANN 1999, 1999

Support vector machines vs multi-layer perceptrons in particle identification.
Proceedings of the ESANN 1999, 1999

A Note on Support Vector Machine Degeneracy.
Proceedings of the Algorithmic Learning Theory, 10th International Conference, 1999

On the V<sub>gamma</sub> Dimension for Regression in Reproducing Kernel Hilbert Spaces.
Proceedings of the Algorithmic Learning Theory, 10th International Conference, 1999

1998
Support Vector Machines for 3D Object Recognition.
IEEE Trans. Pattern Anal. Mach. Intell., 1998

Properties of Support Vector Machines.
Neural Comput., 1998

Recognizing 3-D Objects with Linear Support Vector Machines.
Proceedings of the Computer Vision, 1998

1997
Direct Aspect-Based 3D Object Recognition.
Proceedings of the Image Analysis and Processing, 9th International Conference, 1997


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