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    V. Seguy, B. Bhushan Damodaran, R. Flamary, N. Courty, A. Rolet, M. Blondel, "Large-Scale Optimal Transport and Mapping Estimation" (Submited), 2017.
    Abstract: This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions. To that end, we propose a stochastic dual approach of regularized OT, and show empirically that it scales better than a recent related approach when the amount of samples is very large. Second, we estimate a Monge map as a deep neural network learned by approximating the barycentric projection of the previously-obtained OT plan. We prove two theoretical stability results of regularized OT which show that our estimations converge to the OT plan and Monge map between the underlying continuous measures. We showcase our proposed approach on two applications: domain adaptation and generative modeling.
    BibTeX:
    @inproceedings{seguy2017large,
    author = {Seguy, Vivien. and Bhushan Damodaran, Bharath and Flamary, Remi and Courty, Nicolas and Rolet, Antoine and Blondel, Mathieu},
    title = {Large-Scale Optimal Transport and Mapping Estimation},
    year = {2017 (Submited)}
    }
    N. Courty, R. Flamary, M. Ducoffe, "Learning Wasserstein Embeddings" (Submited), 2017.
    Abstract: The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions. It has found numerous applications in several hard problems, such as domain adaptation, dimensionality reduction or generative models. However, its use is still limited by a heavy computational cost. Our goal is to alleviate this problem by providing an approximation mechanism that allows to break its inherent complexity. It relies on the search of an embedding where the Euclidean distance mimics the Wasserstein distance. We show that such an embedding can be found with a siamese architecture associated with a decoder network that allows to move from the embedding space back to the original input space. Once this embedding has been found, computing optimization problems in the Wasserstein space (e.g. barycenters, principal directions or even archetypes) can be conducted extremely fast. Numerical experiments supporting this idea are conducted on image datasets, and show the wide potential benefits of our method.
    BibTeX:
    @inproceedings{courty2017learning,
    author = {Courty, Nicolas and Flamary, Remi and Ducoffe, Melanie},
    title = {Learning Wasserstein Embeddings},
    year = {2017 (Submited)}
    }

    2017

    N. Courty, R. Flamary, A. Habrard, A. Rakotomamonjy, "Joint Distribution Optimal Transportation for Domain Adaptation", Neural Information Processing Systems (NIPS), 2017.
    Abstract: This paper deals with the unsupervised domain adaptation problem, where one wants to estimate a prediction function f in a given target domain without any labeled sample by exploiting the knowledge available from a source domain where labels are known. Our work makes the following assumption: there exists a non-linear transformation between the joint feature/label space distributions of the two domain Ps and Pt. We propose a solution of this problem with optimal transport, that allows to recover an estimated target Pft(X,f(X)) by optimizing simultaneously the optimal coupling and f. We show that our method corresponds to the minimization of a bound on the target error, and provide an efficient algorithmic solution, for which convergence is proved. The versatility of our approach, both in terms of class of hypothesis or loss functions is demonstrated with real world classification and regression problems, for which we reach or surpass state-of-the-art results.
    BibTeX:
    @inproceedings{courty2017joint,
    author = {Courty, Nicolas and Flamary, Remi and Habrard, Amaury and Rakotomamonjy, Alain},
    title = {Joint Distribution Optimal Transportation for Domain Adaptation},
    booktitle = {Neural Information Processing Systems (NIPS)},
    year = {2017}
    }
    R. Mourya, A. Ferrari, R. Flamary, P. Bianchi, C. Richard, "Distributed Approach for Deblurring Large Images with Shift-Variant Blur", European Conference on Signal Processing (EUSIPCO), 2017.
    Abstract: Image deblurring techniques are effective tools to obtain high quality image from acquired image degraded by blur and noise. In applications such as astronomy and satellite imaging, size of acquired images can be extremely large (up to gigapixels) covering a wide field-of-view suffering from shift-variant blur. Most of the existing deblurring techniques are designed to be cost effective on a centralized computing system having a shared memory and possibly multicore processor. The largest image they can handle is then conditioned by the memory capacity of the system. In this paper, we propose a distributed shift-variant image deblurring algorithm in which several connected processing units (each with reasonable computational resources) can deblur simultaneously different portions of a large image while maintaining a certain coherency among them to finally obtain a single crisp image. The proposed algorithm is based on a distributed Douglas-Rachford splitting algorithm with a specific structure of the penalty parameters used in the proximity operator. Numerical experiments show that the proposed algorithm produces images of similar quality as the existing centralized techniques while being distributed and being cost effective for extremely large images.
    BibTeX:
    @inproceedings{mourya2017distributed,
    author = {Mourya, Rahul and Ferrari, Andre and Flamary, Remi and Bianchi, Pascal and Richard, Cedric},
    title = {Distributed Approach for Deblurring Large Images with Shift-Variant Blur},
    booktitle = {European Conference on Signal Processing (EUSIPCO)},
    year = {2017}
    }
    R. Flamary, "Astronomical image reconstruction with convolutional neural networks", European Conference on Signal Processing (EUSIPCO), 2017.
    Abstract: State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. Solving this problem can be computationally intensive and usually leads to a quadratic or at least superlinear complexity w.r.t. the number of pixels in the image. We investigate in this work the use of convolutional neural networks for image reconstruction in astronomy. With neural networks, the computationally intensive tasks is the training step, but the prediction step has a fixed complexity per pixel, i.e. a linear complexity. Numerical experiments show that our approach is both computationally efficient and competitive with other state of the art methods in addition to being interpretable.
    BibTeX:
    @inproceedings{flamary2017astro,
    author = {Flamary, Remi},
    title = {Astronomical image reconstruction with convolutional neural networks},
    booktitle = {European Conference on Signal Processing (EUSIPCO)},
    year = {2017}
    }
    R. Ammanouil, A. Ferrari, R. Flamary, C. Ferrari, D. Mary, "Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters", European Conference on Signal Processing (EUSIPCO), 2017.
    Abstract: As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image reconstruction For radio INterferometry). In particular, we focus on the challenging task of automatically finding the optimal regularization parameter values. In practice, finding the regularization parameters using classical grid search is computationally intensive and nontrivial due to the lack of ground- truth. We adopt a greedy strategy where, at each iteration, the optimal parameters are found by minimizing the predicted Stein unbiased risk estimate (PSURE). The proposed self-tuned version of MUFFIN involves parallel and computationally efficient steps, and scales well with large- scale data. Finally, numerical results on a 3D image are presented to showcase the performance of the proposed approach.
    BibTeX:
    @inproceedings{ammanouil2017multi,
    author = {Ammanouil, Rita and Ferrari, Andre and Flamary, Remi and Ferrari, Chiara and Mary, David},
    title = {Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters},
    booktitle = {European Conference on Signal Processing (EUSIPCO)},
    year = {2017}
    }

    2016

    R. Flamary, C. Févotte, N. Courty, V. Emyia, "Optimal spectral transportation with application to music transcription", Neural Information Processing Systems (NIPS), 2016.
    Abstract: Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates. In particular, state-of-the-art music transcription systems decompose the spectrogram of the input signal onto a dictionary of representative note spectra. The typical measures of fit used to quantify the adequacy of the decomposition compare the data and template entries frequency-wise. As such, small displacements of energy from a frequency bin to another as well as variations of timber can disproportionally harm the fit. We address these issues by means of optimal transportation and propose a new measure of fit that treats the frequency distributions of energy holistically as opposed to frequency-wise. Building on the harmonic nature of sound, the new measure is invariant to shifts of energy to harmonically-related frequencies, as well as to small and local displacements of energy. Equipped with this new measure of fit, the dictionary of note templates can be considerably simplified to a set of Dirac vectors located at the target fundamental frequencies (musical pitch values). This in turns gives ground to a very fast and simple decomposition algorithm that achieves state-of-the-art performance on real musical data.
    BibTeX:
    @inproceedings{flamary2016ost,
    author = {Flamary, Remi and Févotte, Cédric and Courty, N. and  Emyia, Valentin},
    title = {Optimal spectral transportation with application to music transcription},
    booktitle = { Neural Information Processing Systems (NIPS)},
    year = {2016}
    }
    M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
    Abstract: We are interested in the computation of the transport map of an Optimal Transport problem. Most of the computational approaches of Optimal Transport use the Kantorovich relaxation of the problem to learn a probabilistic coupling but do not address the problem of learning the transport map linked to the original Monge problem. Consequently, it lowers the potential usage of such methods in contexts where out-of-samples computations are mandatory. In this paper we propose a new way to jointly learn the coupling and an approximation of the transport map. We use a jointly convex formulation which can be efficiently optimized. Additionally, jointly learning the coupling and the transport map allows to smooth the result of the Optimal Transport and generalize it on out-of-samples examples. Empirically, we show the interest and the relevance of our method in two tasks: domain adaptation and image editing.
    BibTeX:
    @inproceedings{perrot2016mapping,
    author = {Perrot, M. and Courty, N. and Flamary, R. and Habrard, A.},
    title = {Mapping estimation for discrete optimal transport},
    booktitle = {Neural Information Processing Systems (NIPS)},
    year = {2016}
    }
    I. Harrane, R. Flamary, C. Richard, "Doubly partial-diffusion LMS over adaptive networks", Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2016.
    Abstract: Diffusion LMS is an efficient strategy for solving distributed optimization problems with cooperating agents. Nodes are interested in estimating the same parameter vector and exchange information with their neighbors to improve their local estimates. However, successful implementation of such applications depends on a substantial amount of communication resources. In this paper, we introduce diffusion algorithms that have a significantly reduced communication load without compromising performance. We also perform analyses in the mean and mean-square sense. Simulations results are provided to confirm the theoretical findings.
    BibTeX:
    @inproceedings{harrane2016doubly,
    author = {Harrane, Ibrahim and Flamary, R. and Richard, C.},
    title = {Doubly partial-diffusion LMS over adaptive networks},
    booktitle = {Asilomar Conference on Signals, Systems and Computers (ASILOMAR)},
    year = {2016}
    }
    S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, "Supervised planetary unmixing with optimal transport", Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016.
    Abstract: This paper is focused on spectral unmixing and present an original technique based on Optimal Transport. Optimal Transport consists in estimating a plan that transports a spectrum onto another with minimal cost, enabling to compute an associated distance (Wasserstein distance) that can be used as an alternative metric to compare hyperspectral data. This is exploited for spectral unmixing where abundances in each pixel are estimated on the basis of their projections in a Wasserstein sense (Bregman projections) onto known endmembers. In this work an over-complete dictionary is used to deal with internal variability between endmembers, while a regularization term, also based on Wasserstein distance, is used to promote prior proportion knowledge in the endmember groups. Experiments are performed on real hyperspectral data of asteroid 4-Vesta.
    BibTeX:
    @inproceedings{nakhostin2016planetary,
    author = {Nakhostin, Sina  and Courty, Nicolas and Flamary, Remi and Tuia, D. and Corpetti, Thomas},
    title = {Supervised planetary unmixing with optimal transport},
    booktitle = {Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS)},
    year = {2016}
    }
    N. Courty, R. Flamary, D. Tuia, T. Corpetti, "Optimal transport for data fusion in remote sensing", International Geoscience and Remote Sensing Symposium (IGARSS), 2016.
    Abstract: One of the main objective of data fusion is the integration of several acquisition of the same physical object, in order to build a new consistent representation that embeds all the information from the different modalities. In this paper, we propose the use of optimal transport theory as a powerful mean of establishing correspondences between the modalities. After reviewing important properties and computational aspects, we showcase its application to three remote sensing fusion problems: domain adaptation, time series averaging and change detection in LIDAR data.
    BibTeX:
    @inproceedings{courty2016optimalrs,
    author = {Courty, N. and Flamary, R. and Tuia, D. and Corpetti, T.},
    title = {Optimal transport for data fusion in remote sensing},
    booktitle = {International Geoscience and Remote Sensing Symposium (IGARSS)},
    year = {2016}
    }
    I. Harrane, R. Flamary, C. Richard, "Toward privacy-preserving diffusion strategies for adaptation and learning over networks", European Conference on Signal Processing (EUSIPCO), 2016.
    Abstract: Distributed optimization allows to address inference problems in a decentralized manner over networks, where agents can exchange information with their neighbors to improve their local estimates. Privacy preservation has become an important issue in many data mining applications. It aims at protecting the privacy of individual data in order to prevent the disclosure of sensitive information during the learning process. In this paper, we derive a diffusion strategy of the LMS type to solve distributed inference problems in the case where agents are also interested in preserving the privacy of the local measurements. We carry out a detailed mean and mean-square error analysis of the algorithm. Simulations are provided to check the theoretical findings.
    BibTeX:
    @inproceedings{haranne2016toward,
    author = {Harrane, I. and Flamary, R. and Richard, C.},
    title = {Toward privacy-preserving diffusion strategies for adaptation and learning over networks},
    booktitle = {European Conference on Signal Processing (EUSIPCO)},
    year = {2016}
    }

    2015

    R. Flamary, A. Rakotomamonjy, G. Gasso, "Importance Sampling Strategy for Non-Convex Randomized Block-Coordinate Descent", IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015.
    Abstract: As the number of samples and dimensionality of optimization problems related to statistics and machine learning explode, block coordinate descent algorithms have gained popularity since they reduce the original problem to several smaller ones. Coordinates to be optimized are usually selected randomly according to a given probability distribution. We introduce an importance sampling strategy that helps randomized coordinate descent algorithms to focus on blocks that are still far from convergence. The framework applies to problems composed of the sum of two possibly non-convex terms, one being separable and non-smooth. We have compared our algorithm to a full gradient proximal approach as well as to a randomized block coordinate algorithm that considers uniform sampling and cyclic block coordinate descent. Experimental evidences show the clear benefit of using an importance sampling strategy.
    BibTeX:
    @inproceedings{flamary2015importance,
    author = {Flamary, R. and Rakotomamonjy, A. and  Gasso, G.},
    title = {Importance Sampling Strategy for Non-Convex Randomized Block-Coordinate Descent},
    booktitle = {IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
    year = {2015}
    }
    R. Flamary, I. Harrane, M. Fauvel, S. Valero, M. Dalla Mura, "Discrimination périodique à partir d’observations multi-temporelles", GRETSI, 2015.
    Abstract: In this work, we propose a novel linear classification scheme for non-stationary periodic data. We express the classifier in a temporal basis while regularizing its temporal complexity leading to a convex optimization problem. Numerical experiments show very good results on a simulated example and on real life remote sensing image classification problem.
    BibTeX:
    @inproceedings{flamary2015discrimination,
    author = {Flamary, R. and Harrane, I. and Fauvel, M. and Valero, S. and Dalla Mura, M.},
    title = {Discrimination périodique à partir d’observations multi-temporelles},
    booktitle = {GRETSI},
    year = {2015}
    }
    D. Tuia, R. Flamary, A. Rakotomamonjy, N. Courty, "Multitemporal classification without new labels: a solution with optimal transport", International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multitemp), 2015.
    Abstract: We propose to adapt distributions between couples of remote sensing images with regularized optimal transport: we apply two forms of regularizations, namely an entropy-based regularization and a class-based regularization to a series of classification problems involving very high resolution images acquired by the WorldView2 satellite. We study the effect of the two regularizers on the quality of the transport.
    BibTeX:
    @inproceedings{tuia2015multitemporal,
    author = {Tuia, D. and Flamary, R. and Rakotomamonjy, A. and  Courty, N.},
    title = {Multitemporal classification without new labels: a solution with optimal transport},
    booktitle = {International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multitemp)},
    year = {2015}
    }
    D. Tuia, R. Flamary, M. Barlaud, "To be or not to be convex? A study on regularization in hyperspectral image classification", International Geoscience and Remote Sensing Symposium (IGARSS), 2015.
    Abstract: Hyperspectral image classification has long been dominated by convex models, which provide accurate decision functions exploiting all the features in the input space. However, the need for high geometrical details, which are often satisfied by using spatial filters, and the need for compact models (i.e. relying on models issued form reduced input spaces) has pushed research to study alternatives such as sparsity inducing regularization, which promotes models using only a subset of the input features. Although successful in reducing the number of active inputs, these models can be biased and sometimes offer sparsity at the cost of reduced accuracy. In this paper, we study the possibility of using non-convex regularization, which limits the bias induced by the regularization. We present and compare four regularizers, and then apply them to hyperspectral classification with different cost functions.
    BibTeX:
    @inproceedings{tuia2015tobe,
    author = {Tuia, D. and Flamary, R. and Barlaud, M.},
    title = {To be or not to be convex? A study on regularization in   hyperspectral image classification},
    booktitle = {International Geoscience and Remote Sensing Symposium (IGARSS)},
    year = {2015}
    }

    2014

    A. Boisbunon, R. Flamary, A. Rakotomamonjy, A. Giros, J. Zerubia, "Large scale sparse optimization for object detection in high resolution images", IEEE Workshop in Machine Learning for Signal Processing (MLSP), 2014.
    Abstract: In this work, we address the problem of detecting objects in images by expressing the image as convolutions between activation matrices and dictionary atoms. The activation matrices are estimated through sparse optimization and correspond to the position of the objects. In particular, we propose an efficient algorithm based on an active set strategy that is easily scalable and can be computed in parallel. We apply it to a toy image and a satellite image where the aim is to detect all the boats in a harbor. These results show the benefit of using nonconvex penalties, such as the log-sum penalty, over the convex l1 penalty.
    BibTeX:
    @inproceedings{boisbunon2014largescale,
    author = {Boisbunon, A. and Flamary, R. and Rakotomamonjy, A. and Giros, A. and Zerubia, J.},
    title = {Large scale sparse optimization for object detection in high resolution images},
    booktitle = {IEEE Workshop in Machine Learning for Signal Processing (MLSP)},
    year = {2014}
    }
    E. Niaf, R. Flamary, A. Rakotomamonjy, O. Rouvière, C. Lartizien, "SVM with feature selection and smooth prediction in images: application to CAD of prostate cancer", IEEE International Conference on Image Processing (ICIP), 2014.
    Abstract: We propose a new computer-aided detection scheme for prostate cancer screening on multiparametric magnetic resonance (mp-MR) images. Based on an annotated training database of mp-MR images from thirty patients, we train a novel support vector machine (SVM)-inspired classifier which simultaneously learns an optimal linear discriminant and a subset of predictor variables (or features) that are most relevant to the classification task, while promoting spatial smoothness of the malignancy prediction maps. The approach uses a $\ell_1$-norm in the regularization term of the optimization problem that rewards sparsity. Spatial smoothness is promoted via an additional cost term that encodes the spatial neighborhood of the voxels, to avoid noisy prediction maps. Experimental comparisons of the proposed $\ell_1$-Smooth SVM scheme to the regular $\ell_2$-SVM scheme demonstrate a clear visual and numerical gain on our clinical dataset.
    BibTeX:
    @inproceedings{niaf2014svmsmooth,
    author = {Niaf, E. and Flamary, R. and Rakotomamonjy, A. and Rouvière, O. and Lartizien, C.},
    title = {SVM with feature selection and smooth prediction in images: application to CAD of prostate cancer},
    booktitle = {IEEE International Conference on Image Processing (ICIP)},
    year = {2014}
    }
    D. Tuia, N. Courty, R. Flamary, "A group-lasso active set strategy for multiclass hyperspectral image classification", Photogrammetric Computer Vision (PCV), 2014.
    Abstract: Hyperspectral images have a strong potential for landcover/landuse classification, since the spectra of the pixels can highlight subtle differences between materials and provide information beyond the visible spectrum. Yet, a limitation of most current approaches is the hypothesis of spatial independence between samples: images are spatially correlated and the classification map should exhibit spatial regularity. One way of integrating spatial smoothness is to augment the input spectral space with filtered versions of the bands. However, open questions remain, such as the selection of the bands to be filtered, or the filterbank to be used. In this paper, we consider the entirety of the possible spatial filters by using an incremental feature learning strategy that assesses whether a candidate feature would improve the model if added to the current input space. Our approach is based on a multiclass logistic classifier with group-lasso regularization. The optimization of this classifier yields an optimality condition, that can easily be used to assess the interest of a candidate feature without retraining the model, thus allowing drastic savings in computational time. We apply the proposed method to three challenging hyperspectral classification scenarios, including agricultural and urban data, and study both the ability of the incremental setting to learn features that always improve the model and the nature of the features selected.
    BibTeX:
    @inproceedings{tuia2014grouplasso,
    author = {Tuia, D. and Courty, N. and Flamary, R.},
    title = {A group-lasso active set strategy for multiclass hyperspectral image classification},
    booktitle = {Photogrammetric Computer Vision (PCV)},
    year = {2014}
    }
    J. Lehaire, R. Flamary, O. Rouvière, C. Lartizien, "Computer-aided diagnostic for prostate cancer detection and characterization combining learned dictionaries and supervised classification", IEEE International Conference on Image Processing (ICIP), 2014.
    Abstract: This paper aims at presenting results of a computer-aided diagnostic (CAD) system for voxel based detection and characterization of prostate cancer in the peripheral zone based on multiparametric magnetic resonance (mp-MR) imaging. We propose an original scheme with the combination of a feature extraction step based on a sparse dictionary learning (DL) method and a supervised classification in order to discriminate normal (N), normal but suspect (NS) tissues as well as different classes of cancer tissue whose aggressiveness is characterized by the Gleason score ranging from 6 (GL6) to 9 (GL9). We compare the classification performance of two supervised methods, the linear support vector machine (SVM) and the multinomial logistic regression (MLR) classifiers in a binary classification task. Classification performances were evaluated over an mp-MR image database of 35 patients where each voxel was labeled, based on a ground truth, by an expert radiologist. Results show that the proposed method in addition to being interpretable thanks to the sparse representation of the voxels compares favorably (AUC>0.8) with recent state of the art performances. Preliminary results on example patients data also indicate that the outputs cancer probability maps are correlated to the Gleason score.
    BibTeX:
    @inproceedings{lehaire2014dicolearn,
    author = {Lehaire, J. and Flamary, R. and Rouvière, O. and Lartizien, C.},
    title = {Computer-aided diagnostic for prostate cancer detection and characterization combining learned dictionaries and supervised
    classification},
    booktitle = {IEEE International Conference on Image Processing (ICIP)},
    year = {2014}
    }
    A. Ferrari, D. Mary, R. Flamary, C. Richard, "Distributed image reconstruction for very large arrays in radio astronomy", IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014.
    Abstract: Current and future radio interferometric arrays such as LOFAR and SKA are characterized by a paradox. Their large number of receptors (up to millions) allow theoretically unprecedented high imaging resolution. In the same time, the ultra massive amounts of samples makes the data transfer and computational loads (correlation and calibration) order of magnitudes too high to allow any currently existing image reconstruction algorithm to achieve, or even approach, the theoretical resolution. We investigate here decentralized and distributed image reconstruction strategies which select, transfer and process only a fraction of the total data. The loss in MSE incurred by the proposed approach is evaluated theoretically and numerically on simple test cases.
    BibTeX:
    @inproceedings{ferrari2014distributed,
    author = {Ferrari, A. and Mary, D. and Flamary, R. and Richard, C.},
    title = {Distributed image reconstruction for very large arrays in radio astronomy},
    booktitle = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
    year = {2014}
    }
    N. Courty, R. Flamary, D. Tuia, "Domain adaptation with regularized optimal transport", European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2014.
    Abstract: We present a new and original method to solve the domain adaptation problem using optimal transport. By searching for the best transportation plan between the probability distribution functions of a source and a target domain, a non-linear and invertible transformation of the learning samples can be estimated. Any standard machine learning method can then be applied on the transformed set, which makes our method very generic. We propose a new optimal transport algorithm that incorporates label information in the optimization: this is achieved by combining an efficient matrix scaling technique together with a majoration of a non-convex regularization term. By using the proposed optimal transport with label regularization, we obtain significant increase in performance compared to the original transport solution. The proposed algorithm is computationally efficient and effective, as illustrated by its evaluation on a toy example and a challenging real life vision dataset, against which it achieves competitive results with respect to state-of-the-art methods.
    BibTeX:
    @inproceedings{courty2014domain,
    author = {Courty, N. and Flamary, R. and Tuia, D.},
    title = {Domain adaptation with regularized optimal transport},
    booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)},
    year = {2014}
    }
    A. Boisbunon, R. Flamary, A. Rakotomamonjy, "Active set strategy for high-dimensional non-convex sparse optimization problems", International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2014.
    Abstract: The use of non-convex sparse regularization has attracted much interest when estimating a very sparse model on high dimensional data. In this work we express the optimality conditions of the optimization problem for a large class of non-convex regularizers. From those conditions, we derive an efficient active set strategy that avoids the computing of unnecessary gradients. Numerical experiments on both generated and real life datasets show a clear gain in computational cost w.r.t. the state of the art when using our method to obtain very sparse solutions.
    BibTeX:
    @inproceedings{boisbunon2014active,
    author = {Boisbunon, A. and Flamary, R. and Rakotomamonjy, A.},
    title = {Active set strategy for high-dimensional non-convex sparse optimization problems},
    booktitle = {International Conference on Acoustic, Speech and Signal Processing (ICASSP)},
    year = {2014}
    }

    2013

    W. Gao, J. Chen, C. Richard, J. Huang, R. Flamary, "Kernel LMS algorithm with Forward-Backward splitting for dictionnary learning", International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2013.
    Abstract: Nonlinear adaptive filtering with kernels has become a topic of high interest over the last decade. A characteristics of kernel-based techniques is that they deal with kernel expansions whose number of terms is equal to the number of input data, making them unsuitable for online applications. Kernel-based adaptive filtering algorithms generally rely on a two-stage process at each iteration: a model order control stage that limits the increase in the number of terms by including only valuable kernels into the so-called dictionary, and a fil- ter parameter update stage. It is surprising to note that most existing strategies for dictionary update can only incorporate new elements into the dictionary. This unfortunately means that they cannot discard obsolete kernel functions, within the context of a time-varying environment in particular. Recently, to remedy this drawback, it has been proposed to associate an l1-norm regularization criterion with the mean-square error criterion. The aim of this paper is to provide theoretical results on the convergence of this approach.
    BibTeX:
    @inproceedings{gao2013kernel,
    author = {Gao, W. and Chen, J. and Richard, C. and Huang, J. and Flamary, R.},
    title = {Kernel LMS algorithm with Forward-Backward splitting for dictionnary learning},
    booktitle = {International Conference on Acoustic, Speech and Signal Processing (ICASSP)},
    year = {2013}
    }
    R. Flamary, A. Rakotomamonjy, "Support Vector Machine with spatial regularization for pixel classification", International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines : theory and applications (ROKS), 2013.
    Abstract: We propose in this work to regularize the output of a svm classifier on pixels in order to promote smoothness in the predicted image. The learning problem can be cast as a semi-supervised SVM with a particular structure encoding pixel neighborhood in the regularization graph. We provide several optimization schemes in order to solve the problem for linear SVM with l2 or l1 regularization and show the interest of the approach on an image classification example with very few labeled pixels.
    BibTeX:
    @inproceedings{ROKS2013,
    author = {  Flamary, R. and  Rakotomamonjy, A.},
    title = {Support Vector Machine with spatial regularization for pixel classification},
    booktitle = { International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines : theory and applications (ROKS)},
    year = {2013}
    }
    D. Tuia, M. Volpi, M. Dalla Mura, A. Rakotomamonjy, R. Flamary, "Create the relevant spatial filterbank in the hyperspectral jungle", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2013.
    Abstract: Inclusion of spatial information is known to be beneficial to the classification of hyperspectral images. However, given the high dimensionality of the data, it is difficult to know before hand which are the bands to filter or what are the filters to be applied. In this paper, we propose an active set algorithm based on a $l_1$ support vector machine that explores the (possibily infinite) space of spatial filters and retrieve automatically the filters that maximize class separation. Experiments on hyperspectral imagery confirms the power of the method, that reaches state of the art performance with small feature sets generated automatically and without prior knowledge.
    BibTeX:
    @inproceedings{IGARSS2013,
    author = {  Tuia, D. and  Volpi, M. and  Dalla Mura, M. and  Rakotomamonjy, A. and  Flamary, R.},
    title = {Create the relevant spatial filterbank in the hyperspectral jungle},
    booktitle = { IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
    year = {2013}
    }

    2012

    D. Tuia, R. Flamary, M. Volpi, M. Dalla Mura, A. Rakotomamonjy, " Discovering relevant spatial filterbanks for VHR image classification", International Conference on Pattern Recognition (ICPR), 2012.
    Abstract: In very high resolution (VHR) image classification it is common to use spatial filters to enhance the discrimination among landuses related to similar spectral properties but different spatial characteristics. However, the filters types that can be used are numerous (e.g. textural, morphological, Gabor, wavelets, etc.) and the user must pre-select a family of features, as well as their specific parameters. This results in features spaces that are high dimensional and redundant, thus requiring long and suboptimal feature selection phases. In this paper, we propose to discover the relevant filters as well as their parameters with a sparsity promoting regularization and an active set algorithm that iteratively adds to the model the most promising features. This way, we explore the filters/parameters input space efficiently (which is infinitely large for continuous parameters) and construct the optimal filterbank for classification without any other information than the types of filters to be used.
    BibTeX:
    @inproceedings{ICPR2012,
    author = {  Tuia, D. and  Flamary, R. and  Volpi, M. and  Dalla Mura, M. and  Rakotomamonjy, A.},
    title = { Discovering relevant spatial filterbanks for VHR image classification},
    booktitle = { International Conference on Pattern Recognition (ICPR)},
    year = {2012}
    }
    E. Niaf, R. Flamary, S. Canu, O. Rouvière, C. Lartizien, "Handling learning samples uncertainties in SVM : application to MRI-based prostate cancer Computer-Aided Diagnosis", IEEE International Symposium on Biomedical Imaging , 2012.
    Abstract: Building an accurate training database is challenging in supervised classification. Radiologists often delineate malignant and benign tissues without access to the ground truth thus leading to uncertain datasets. We propose to deal with this uncertainty by introducing probabilistic labels in the learning stage. We introduce a probabilistic support vector machine (P-SVM) inspired from the regular C-SVM formulation allowing to consider class labels through a hinge loss and probability estimates using epsilon-insensitive cost function together with a minimum norm (maximum margin) objective. Solution is used for both decision and posterior probability estimation.
    BibTeX:
    @inproceedings{isbi2012,
    author = { Niaf, E. and Flamary, R. and Canu, S. and Rouvière, O. and Lartizien, C.},
    title = {Handling learning samples uncertainties in SVM : application to MRI-based prostate cancer Computer-Aided Diagnosis},
    booktitle = { IEEE International Symposium on Biomedical Imaging },
    year = {2012}
    }

    2011

    R. Flamary, F. Yger, A. Rakotomamonjy, " Selecting from an infinite set of features in SVM", European Symposium on Artificial Neural Networks, 2011.
    Abstract: Dealing with the continuous parameters of a feature extraction method has always been a difficult task that is usually solved by cross-validation. In this paper, we propose an active set algorithm for selecting automatically these parameters in a SVM classification context. Our experiments on texture recognition and BCI signal classification show that optimizing the feature parameters in a continuous space while learning the decision function yields to better performances than using fixed parameters obtained from a grid sampling
    BibTeX:
    @inproceedings{ESANN2011,
    author = { Flamary, R. and Yger, F. and Rakotomamonjy, A.},
    title = { Selecting from an infinite set of features in SVM},
    booktitle = { European Symposium on Artificial Neural Networks},
    year = {2011}
    }
    R. Flamary, X. Anguera, N. Oliver, " Spoken WordCloud: Clustering Recurrent Patterns in Speech", International Workshop on Content-Based Multimedia Indexing, 2011.
    Abstract: The automatic summarization of speech recordings is typically carried out as a two step process: the speech is first decoded using an automatic speech recognition system and the resulting text transcripts are processed to create the summary. However, this approach might not be suitable with adverse acoustic conditions or languages with limited training resources. In order to address these limitations, we propose in this paper an automatic speech summarization method that is based on the automatic discovery of patterns in the speech: recurrent acoustic patterns are first extracted from the audio and then are clustered and ranked according to the number of repetitions in the recording. This approach allows us to build what we call a Spoken WordCloud because of its similarity with text-based word-clouds. We present an algorithm that achieves a cluster purity of up to 90% and an inverse purity of 71% in preliminary experiments using a small dataset of connected spoken words.
    BibTeX:
    @inproceedings{CBMI2011,
    author = { Flamary, R. and Anguera, X. and Oliver, N.},
    title = { Spoken WordCloud: Clustering Recurrent Patterns in Speech},
    booktitle = { International Workshop on Content-Based Multimedia Indexing},
    year = {2011}
    }
    E. Niaf, R. Flamary, C. Lartizien, S. Canu, "Handling uncertainties in SVM classification", IEEE Workshop on Statistical Signal Processing , 2011.
    Abstract: This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here is either qualitative (a class label) or quantitative (an estimation of the posterior probability). Our main contribution is a SVM inspired formulation of this problem allowing to take into account class label through a hinge loss as well as probability estimates using epsilon-insensitive cost function together with a minimum norm (maximum margin) objective. This formulation shows a dual form leading to a quadratic problem and allows the use of a representer theorem and associated kernel. The solution provided can be used for both decision and posterior probability estimation. Based on empirical evidence our method outperforms regular SVM in terms of probability predictions and classification performances.
    BibTeX:
    @inproceedings{ssp2011,
    author = { Niaf, E. and Flamary, R. and Lartizien, C. and Canu, S.},
    title = {Handling uncertainties in SVM classification},
    booktitle = { IEEE Workshop on Statistical Signal Processing },
    year = {2011}
    }

    2010

    R. Flamary, B. Labbé, A. Rakotomamonjy, "Large margin filtering for signal sequence labeling", International Conference on Acoustic, Speech and Signal Processing 2010, 2010.
    Abstract: Signal Sequence Labeling consists in predicting a sequence of labels given an observed sequence of samples. A naive way is to filter the signal in order to reduce the noise and to apply a classification algorithm on the filtered samples. We propose in this paper to jointly learn the filter with the classifier leading to a large margin filtering for classification. This method allows to learn the optimal cutoff frequency and phase of the filter that may be different from zero. Two methods are proposed and tested on a toy dataset and on a real life BCI dataset from BCI Competition III.
    BibTeX:
    @inproceedings{flamaryicassp210,
    author = { Flamary, R. and Labbé, B. and Rakotomamonjy, A.},
    title = {Large margin filtering for signal sequence labeling},
    booktitle = { International Conference on Acoustic, Speech and Signal Processing  2010},
    year = {2010}
    }
    R. Flamary, B. Labbé, A. Rakotomamonjy, "Filtrage vaste marge pour l'étiquetage séquentiel de signaux", Conference en Apprentissage CAp, 2010.
    Abstract: Ce papier traite de l’étiquetage séquentiel de signaux, c’est-à-dire de discrimination pour des échantillons temporels. Dans ce contexte, nous proposons une méthode d’apprentissage pour un filtrage vaste-marge séparant au mieux les classes. Nous apprenons ainsi de manière jointe un SVM sur des échantillons et un filtrage temporel de ces échantillons. Cette méthode permet l’étiquetage en ligne d’échantillons temporels. Un décodage de séquence hors ligne optimal utilisant l’algorithme de Viterbi est également proposé. Nous introduisons différents termes de régularisation, permettant de pondérer ou de sélectionner les canaux automatiquement au sens du critère vaste-marge. Finalement, notre approche est testée sur un exemple jouet de signaux non-linéaires ainsi que sur des données réelles d’Interface Cerveau-Machine. Ces expériences montrent l’intérêt de l’apprentissage supervisé d’un filtrage temporel pour l’étiquetage de séquence.
    BibTeX:
    @inproceedings{flamcap2010,
    author = { Flamary, R. and Labbé, B. and Rakotomamonjy, A.},
    title = {Filtrage vaste marge pour l'étiquetage séquentiel de signaux},
    booktitle = { Conference en Apprentissage CAp},
    year = {2010}
    }
    D. Tuia, G. Camps-Valls, R. Flamary, A. Rakotomamonjy, "Learning spatial filters for multispectral image segmentation", IEEE Workshop in Machine Learning for Signal Processing (MLSP), 2010.
    Abstract: We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters
    BibTeX:
    @inproceedings{mlsp10,
    author = { Tuia, D. and Camps-Valls, G. and Flamary, R. and Rakotomamonjy, A.},
    title = {Learning spatial filters for multispectral image segmentation},
    booktitle = { IEEE Workshop in Machine Learning for Signal Processing (MLSP)},
    year = {2010}
    }

    2009

    R. Flamary, A. Rakotomamonjy, G. Gasso, S. Canu, "Selection de variables pour l'apprentissage simultanée de tâches", Conférence en Apprentissage (CAp'09), 2009.
    Abstract: Cet article traite de la sélection de variables pour l’apprentissage simultanée de taches de discrimination SVM . Nous formulons ce problème comme étant un apprentissage multi-taches avec pour terme de régularisation une norme mixte de type `p `2 avec p <1 . Cette dernière permet d’obtenir des modèles de discrimination pour chaque tâche, utilisant un même sous-ensemble des variables. Nous proposons tout d’abord un algorithme permettant de résoudre le problème d’apprentissage lorsque la norme mixte est convexe (p = 1). Ensuite, à l’aide de la programmation DC, nous traitons le cas non-convexe (p < 1) . Nous montrons que ce dernier cas peut être résolu par un algorithme itératif où, à chaque itération, un problème basé sur la norme mixte `1 `2 est résolu. Nos expériences montrent l’interêt de la méthode sur quelques problèmes de discriminations simultanées.
    BibTeX:
    @inproceedings{cap09,
    author = { Flamary, R. and Rakotomamonjy, A. and Gasso, G. and Canu, S.},
    title = {Selection de variables pour l'apprentissage simultanée de tâches},
    booktitle = { Conférence en Apprentissage (CAp'09)},
    year = {2009}
    }
    R. Flamary, J. Rose, A. Rakotomamonjy, S. Canu, "Variational Sequence Labeling", IEEE Workshop in Machine Learning for Signal Processing (MLSP), 2009.
    Abstract: Sequence labeling is concerned with processing an input data sequence and producing an output sequence of discrete labels which characterize it. Common applications includes speech recognition, language processing (tagging, chunking) and bioinformatics. Many solutions have been proposed to partially cope with this problem. These include probabilistic models (HMMs, CRFs) and machine learning algorithm (SVM, Neural nets). In practice, the best results have been obtained by combining several of these methods. However, fusing different signal segmentation methods is not straightforward, particularly when integrating prior information. In this paper the sequence labeling problem is viewed as a multi objective optimization task. Each objective targets a different aspect of sequence labelling such as good classification, temporal stability and change detection. The resulting optimization problem turns out to be non convex and plagued with numerous local minima. A region growing algorithm is proposed as a method for finding a solution to this multi functional optimization task. The proposed algorithm is evaluated on both synthetic and real data (BCI dataset). Results are encouraging and better than those previously reported on these datasets.
    BibTeX:
    @inproceedings{mlsp09,
    author = { R. Flamary and J.L. Rose and A. Rakotomamonjy and S. Canu},
    title = {Variational Sequence Labeling},
    booktitle = { IEEE Workshop in Machine Learning for Signal Processing (MLSP)},
    year = {2009}
    }