2025
Provably accurate adaptive sampling for collocation points in physics-informed neural networks.
CoRR, April, 2025
2024
Approximation Error of Sobolev Regular Functions with Tanh Neural Networks: Theoretical Impact on PINNs.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024
Generative shape deformation with optimal transport using learned transformations.
Proceedings of the International Joint Conference on Neural Networks, 2024
Unsupervised Learning and Effective Complexity: Introducing JPG and Neural Sophistication.
Proceedings of the 36th IEEE International Conference on Tools with Artificial Intelligence, 2024
Physics-Informed Machine Learning for Better Understanding Laser-Matter Interaction.
Proceedings of the 36th IEEE International Conference on Tools with Artificial Intelligence, 2024
2023
Predictive Modeling of Body Shape Changes in Individuals on Dietetic Treatment Using Recurrent Networks.
Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023), 2023
Is My Neural Net Driven by the MDL Principle?
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023
2022
MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data.
Pattern Recognit. Lett., 2022
Learning PDE to Model Self-Organization of Matter.
Entropy, 2022
Architecture for Automatic Recognition of Group Activities Using Local Motions and Context.
IEEE Access, 2022
Fast Multiscale Diffusion On Graphs.
Proceedings of the IEEE International Conference on Acoustics, 2022
Why do State-of-the-art Super-Resolution Methods not work well for Bone Microstructure CT Imaging?
Proceedings of the 30th European Signal Processing Conference, 2022
Optimal Tensor Transport.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022
2021
Iterative multilinear optimization for planar model fitting under geometric constraints.
PeerJ Comput. Sci., 2021
Sampled Gromov Wasserstein.
Mach. Learn., 2021
A Nearest Neighbor Algorithm for Imbalanced Classification.
Int. J. Artif. Intell. Tools, 2021
Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation.
Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence, 2021
2020
Metric Learning from Imbalanced Data with Generalization Guarantees.
Pattern Recognit. Lett., 2020
A survey on domain adaptation theory.
CoRR, 2020
Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020
Graph Diffusion Wasserstein Distances.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020
Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
Metric Learning in Optimal Transport for Domain Adaptation.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
A Swiss Army Knife for Minimax Optimal Transport.
Proceedings of the 37th International Conference on Machine Learning, 2020
2019
Deep multi-Wasserstein unsupervised domain adaptation.
Pattern Recognit. Lett., 2019
On the analysis of adaptability in multi-source domain adaptation.
Mach. Learn., 2019
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting.
CoRR, 2019
Differentially Private Optimal Transport: Application to Domain Adaptation.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data.
Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019
Metric Learning from Imbalanced Data.
Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019
From Cost-Sensitive to Tight F-measure Bounds.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019
2018
Learning maximum excluding ellipsoids from imbalanced data with theoretical guarantees.
Pattern Recognit. Lett., 2018
Fast and Provably Effective Multi-view Classification with Landmark-Based SVM.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018
Non-Linear Gradient Boosting for Class-Imbalance Learning.
Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2018
Tree-Based Cost Sensitive Methods for Fraud Detection in Imbalanced Data.
Proceedings of the Advances in Intelligent Data Analysis XVII, 2018
Online Non-linear Gradient Boosting in Multi-latent Spaces.
Proceedings of the Advances in Intelligent Data Analysis XVII, 2018
2017
Unsupervised Domain Adaptation Based on Subspace Alignment.
Proceedings of the Domain Adaptation in Computer Vision Applications., 2017
L<sup>3</sup>-SVMs: Landmarks-based Linear Local Support Vectors Machines.
CoRR, 2017
Theoretical Analysis of Domain Adaptation with Optimal Transport.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017
Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017
2016
A new boosting algorithm for provably accurate unsupervised domain adaptation.
Knowl. Inf. Syst., 2016
Learning discriminative tree edit similarities for linear classification - Application to melody recognition.
Neurocomputing, 2016
Lipschitz Continuity of Mahalanobis Distances and Bilinear Forms.
CoRR, 2016
Similarity Learning for Time Series Classification.
CoRR, 2016
Scene classification based on semantic labeling.
Adv. Robotics, 2016
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016
Metric Learning as Convex Combinations of Local Models with Generalization Guarantees.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016
2015
Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 978-3-031-01572-4, 2015
Algorithmic Robustness for Semi-Supervised (ε, γ, τ)-Good Metric Learning.
Proceedings of the 3rd International Conference on Learning Representations, 2015
Computing Image Descriptors from Annotations Acquired from External Tools.
Proceedings of the Robot 2015: Second Iberian Robotics Conference, 2015
Joint Semi-supervised Similarity Learning for Linear Classification.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015
Landmarks-based kernelized subspace alignment for unsupervised domain adaptation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015
Supervised spectral subspace clustering for visual dictionary creation in the context of image classification.
Proceedings of the 3rd IAPR Asian Conference on Pattern Recognition, 2015
2014
Learning a priori constrained weighted majority votes.
Mach. Learn., 2014
Subspace Alignment For Domain Adaptation.
CoRR, 2014
Modeling Perceptual Color Differences by Local Metric Learning.
Proceedings of the Computer Vision - ECCV 2014, 2014
2013
Iterative Self-Labeling Domain Adaptation for Linear Structured Image Classification.
Int. J. Artif. Intell. Tools, 2013
Combining Feature and Prototype Pruning by Uncertainty Minimization
CoRR, 2013
A Survey on Metric Learning for Feature Vectors and Structured Data.
CoRR, 2013
Boosting for Unsupervised Domain Adaptation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013
Unsupervised Visual Domain Adaptation Using Subspace Alignment.
Proceedings of the IEEE International Conference on Computer Vision, 2013
2012
Supervised learning of Gaussian mixture models for visual vocabulary generation.
Pattern Recognit., 2012
Good edit similarity learning by loss minimization.
Mach. Learn., 2012
Speeding Up Syntactic Learning Using Contextual Information.
Proceedings of the Eleventh International Conference on Grammatical Inference, 2012
Similarity Learning for Provably Accurate Sparse Linear Classification.
Proceedings of the 29th International Conference on Machine Learning, 2012
Discriminative feature fusion for image classification.
Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012
2011
Learning Good Edit Similarities with Generalization Guarantees.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011
Domain Adaptation with Good Edit Similarities: A Sparse Way to Deal with Scaling and Rotation Problems in Image Classification.
Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011
Using the H-Divergence to Prune Probabilistic Automata.
Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011
An Experimental Study on Learning with Good Edit Similarity Functions.
Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011
2010
Learning state machine-based string edit kernels.
Pattern Recognit., 2010
Weighted Symbols-Based Edit Distance for String-Structured Image Classification.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010
2009
A lower bound on the sample size needed to perform a significant frequent pattern mining task.
Pattern Recognit. Lett., 2009
Mining probabilistic automata: a statistical view of sequential pattern mining.
Mach. Learn., 2009
Boosting Classifiers Built from Different Subsets of Features.
Fundam. Informaticae, 2009
Discovering Patterns in Flows: A Privacy Preserving Approach with the ACSM Prototype.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009
Learning Constrained Edit State Machines.
Proceedings of the ICTAI 2009, 2009
2008
Learning probabilistic models of tree edit distance.
Pattern Recognit., 2008
Melody Recognition with Learned Edit Distances.
Proceedings of the Structural, 2008
SEDiL: Software for Edit Distance Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008
2007
Correct your text with Google.
Proceedings of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence, 2007
Learning Metrics Between Tree Structured Data: Application to Image Recognition.
Proceedings of the Machine Learning: ECML 2007, 2007
2006
Learning stochastic edit distance: Application in handwritten character recognition.
Pattern Recognit., 2006
Using Learned Conditional Distributions as Edit Distance.
Proceedings of the Structural, 2006
Sequence Mining Without Sequences: A New Way for Privacy Preserving.
Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), 2006
A Discriminative Model of Stochastic Edit Distance in the Form of a Conditional Transducer.
Proceedings of the Grammatical Inference: Algorithms and Applications, 2006
Learning Stochastic Tree Edit Distance.
Proceedings of the Machine Learning: ECML 2006, 2006
2005
Adaptation du boosting à l'inférence grammaticale <i>via</i> l'utilisation d'un oracle de confiance.
Rev. d'Intelligence Artif., 2005
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data.
Fundam. Informaticae, 2005
Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms.
Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, 2005
Constrained Sequence Mining based on Probabilistic Finite State Automata.
Proceedings of the Actes de CAP 05, Conférence francophone sur l'apprentissage automatique, 2005
2004
Mining Decision Rules from Deterministic Finite Automata.
Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), 2004
Boosting grammatical inference with confidence oracles.
Proceedings of the Machine Learning, 2004
2003
A Simple Locally Adaptive Nearest Neighbor Rule With Application To Pollution Forecasting.
Int. J. Pattern Recognit. Artif. Intell., 2003
On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data.
Proceedings of the Machine Learning, 2003
On Boosting Improvement: Error Reduction and Convergence Speed-Up.
Proceedings of the Machine Learning: ECML 2003, 2003
Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference.
Proceedings of the Machine Learning: ECML 2003, 2003
2002
A hybrid filter/wrapper approach of feature selection using information theory.
Pattern Recognit., 2002
Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem.
J. Mach. Learn. Res., 2002
A data-mining approach to spacer oligonucleotide typing of Mycobacterium tuberculosis.
Bioinform., 2002
Boosting Density Function Estimators.
Proceedings of the Machine Learning: ECML 2002, 2002
2001
A Bayesian boosting theorem.
Pattern Recognit. Lett., 2001
An improved bound on the finite-sample risk of the nearest neighbor rule.
Pattern Recognit. Lett., 2001
Advances in Adaptive Prototype Weighting and Selection.
Int. J. Artif. Intell. Tools, 2001
Boosting Neighborhood-Based Classifiers.
Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001
Improvement of Nearest-Neighbor Classifiers via Support Vector Machines.
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference, 2001
2000
Impact of learning set quality and size on decision tree performances.
Int. J. Comput. Syst. Signals, 2000
Combining Feature and Example Pruning by Uncertainty Minimization.
Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000
Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery.
Proceedings of the Principles of Data Mining and Knowledge Discovery, 2000
Instance Pruning as an Information Preserving Problem.
Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000
A Boosting-Based Prototype Weighting and Selection Scheme.
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference, 2000
A Symmetric Nearest Neighbor Learning Rule.
Proceedings of the Advances in Case-Based Reasoning, 5th European Workshop, 2000
Sharper Bounds for the Hardness of Prototype and Feature Selection.
Proceedings of the Algorithmic Learning Theory, 11th International Conference, 2000
Identifying and Eliminating Irrelevant Instances Using Information Theory.
Proceedings of the Advances in Artificial Intelligence, 2000
1999
Selection and Statistical Validation of Features and Prototypes.
Proceedings of the Principles of Data Mining and Knowledge Discovery, 1999
Contribution of Boosting in Wrapper Models.
Proceedings of the Principles of Data Mining and Knowledge Discovery, 1999
Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme.
Proceedings of the Principles of Data Mining and Knowledge Discovery, 1999
From Theoretical Learnability to Statistical Measures of the Learnable.
Proceedings of the Advances in Intelligent Data Analysis, Third International Symposium, 1999
1998
Prototype Selection from Homogeneous Subsets by a Monte Carlo Sampling.
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference, 1998
Strings Clustering and Statistical Validation of Clusters.
Proceedings of the Advances in Artificial Intelligence, 1998
1996
A Comparison of Some Contextual Discretization Methods.
Inf. Sci., 1996