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Marc Sebban

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14 papers
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14

ICML Conference 2025 Conference Paper

A Bregman Proximal Viewpoint on Neural Operators

  • Abdel-Rahim Mezidi
  • Jordan Patracone
  • Saverio Salzo
  • Amaury Habrard
  • Massimiliano Pontil
  • Rémi Emonet
  • Marc Sebban

We present several advances on neural operators by viewing the action of operator layers as the minimizers of Bregman regularized optimization problems over Banach function spaces. The proposed framework allows interpreting the activation operators as Bregman proximity operators from dual to primal space. This novel viewpoint is general enough to recover classical neural operators as well as a new variant, coined Bregman neural operators, which includes the inverse activation operator and features the same expressivity of standard neural operators. Numerical experiments support the added benefits of the Bregman variant of Fourier neural operators for training deeper and more accurate models.

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AAAI Conference 2022 Conference Paper

Optimal Tensor Transport

  • Tanguy Kerdoncuff
  • Rémi Emonet
  • Michael Perrot
  • Marc Sebban

Optimal Transport (OT) has become a popular tool in machine learning to align finite datasets typically lying in the same vector space. To expand the range of possible applications, Co-Optimal Transport (Co-OT) jointly estimates two distinct transport plans, one for the rows (points) and one for the columns (features), to match two data matrices that might use different features. On the other hand, Gromov Wasserstein (GW) looks for a single transport plan from two pairwise intra-domain distance matrices. Both Co-OT and GW can be seen as specific extensions of OT to more complex data. In this paper, we propose a unified framework, called Optimal Tensor Transport (OTT), which takes the form of a generic formulation that encompasses OT, GW and Co-OT and can handle tensors of any order by learning possibly multiple transport plans. We derive theoretical results for the resulting new distance and present an efficient way for computing it. We further illustrate the interest of such a formulation in Domain Adaptation and Comparison-based Clustering.

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ICML Conference 2020 Conference Paper

A Swiss Army Knife for Minimax Optimal Transport

  • Sofien Dhouib
  • Ievgen Redko
  • Tanguy Kerdoncuff
  • Rémi Emonet
  • Marc Sebban

The Optimal transport (OT) problem and its associated Wasserstein distance have recently become a topic of great interest in the machine learning community. However, the underlying optimization problem is known to have two major restrictions: (i) it largely depends on the choice of the cost function and (ii) its sample complexity scales exponentially with the dimension. In this paper, we propose a general formulation of a minimax OT problem that can tackle these restrictions by jointly optimizing the cost matrix and the transport plan, allowing us to define a robust distance between distributions. We propose to use a cutting-set method to solve this general problem and show its links and advantages compared to other existing minimax OT approaches. Additionally, we use this method to define a notion of stability allowing us to select the most robust cost matrix. Finally, we provide an experimental study highlighting the efficiency of our approach.

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IJCAI Conference 2020 Conference Paper

Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data

  • Rémi Viola
  • Rémi Emonet
  • Amaury Habrard
  • Guillaume Metzler
  • Marc Sebban

Learning from imbalanced data, where the positive examples are very scarce, remains a challenging task from both a theoretical and algorithmic perspective. In this paper, we address this problem using a metric learning strategy. Unlike the state-of-the-art methods, our algorithm MLFP, for Metric Learning from Few Positives, learns a new representation that is used only when a test query is compared to a minority training example. From a geometric perspective, it artificially brings positive examples closer to the query without changing the distances to the negative (majority class) data. This strategy allows us to expand the decision boundaries around the positives, yielding a better F-Measure, a criterion which is suited to deal with imbalanced scenarios. Beyond the algorithmic contribution provided by MLFP, our paper presents generalization guarantees on the false positive and false negative rates. Extensive experiments conducted on several imbalanced datasets show the effectiveness of our method.

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IJCAI Conference 2020 Conference Paper

Metric Learning in Optimal Transport for Domain Adaptation

  • Tanguy Kerdoncuff
  • Rémi Emonet
  • Marc Sebban

Domain Adaptation aims at benefiting from a labeled dataset drawn from a source distribution to learn a model from examples generated from a different but related target distribution. Creating a domain-invariant representation between the two source and target domains is the most widely technique used. A simple and robust way to perform this task consists in (i) representing the two domains by subspaces described by their respective eigenvectors and (ii) seeking a mapping function which aligns them. In this paper, we propose to use Optimal Transport (OT) and its associated Wassertein distance to perform this alignment. While the idea of using OT in domain adaptation is not new, the original contribution of this paper is two-fold: (i) we derive a generalization bound on the target error involving several Wassertein distances. This prompts us to optimize the ground metric of OT to reduce the target risk; (ii) from this theoretical analysis, we design an algorithm (MLOT) which optimizes a Mahalanobis distance leading to a transportation plan that adapts better. Extensive experiments demonstrate the effectiveness of this original approach.

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IJCAI Conference 2019 Conference Paper

Differentially Private Optimal Transport: Application to Domain Adaptation

  • Nam LeTien
  • Amaury Habrard
  • Marc Sebban

Optimal transport has received much attention during the past few years to deal with domain adaptation tasks. The goal is to transfer knowledge from a source domain to a target domain by finding a transportation of minimal cost moving the source distribution to the target one. In this paper, we address the challenging task of privacy preserving domain adaptation by optimal transport. Using the Johnson-Lindenstrauss transform together with some noise, we present the first differentially private optimal transport model and show how it can be directly applied on both unsupervised and semi-supervised domain adaptation scenarios. Our theoretically grounded method allows the optimization of the transportation plan and the Wasserstein distance between the two distributions while protecting the data of both domains. We perform an extensive series of experiments on various benchmarks (VisDA, Office-Home and Office-Caltech datasets) that demonstrates the efficiency of our method compared to non-private strategies.

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NeurIPS Conference 2016 Conference Paper

beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data

  • Valentina Zantedeschi
  • Rémi Emonet
  • Marc Sebban

During the past few years, the machine learning community has paid attention to developping new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a generic framework to deal with these weakly labeled scenarios. We introduce the beta-risk as a generalized formulation of the standard empirical risk based on surrogate margin-based loss functions. This risk allows us to express the reliability on the labels and to derive different kinds of learning algorithms. We specifically focus on SVMs and propose a soft margin beta-svm algorithm which behaves better that the state of the art.

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JMLR Journal 2002 Journal Article

Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem

  • Marc Sebban
  • Richard Nock
  • Stéphane Lallich

So far, boosting has been used to improve the quality of moderately accurate learning algorithms, by weighting and combining many of their weak hypotheses into a final classifier with theoretically high accuracy. In a recent work (Sebban, Nock and Lallich, 2001), we have attempted to adapt boosting properties to data reduction techniques. In this particular context, the objective was not only to improve the success rate, but also to reduce the time and space complexities due to the storage requirements of some costly learning algorithms, such as nearest-neighbor classifiers. In that framework, each weak hypothesis, which is usually built and weighted from the learning set, is replaced by a single learning instance. The weight given by boosting defines in that case the relevance of the instance, and a statistical test allows one to decide whether it can be discarded without damaging further classification tasks. In Sebban, Nock and Lallich (2001), we addressed problems with two classes. It is the aim of the present paper to relax the class constraint, and extend our contribution to multiclass problems. Beyond data reduction, experimental results are also provided on twenty-three datasets, showing the benefits that our boosting-derived weighting rule brings to weighted nearest neighbor classifiers.

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