Rémi Emonet's Research Home (see global home)

Selection of Recent Publications (see all publications and details)

Year 2019

Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory

Paul Viallard, Rémi Emonet, Pascal Germain, Amaury Habrard, Emilie Morvant

2019

- NeurIPS 2019 Workshop on Machine Learning with Guarantees

[bibtex] (click to show)

@inproceedings{viallard2019interpreting,
  title={Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory},
  author={Viallard, Paul and Emonet, R{\'e}mi and Germain, Pascal and Habrard, Amaury and Morvant, Emilie},
  booktitle = {NeurIPS 2019 Workshop on Machine Learning with Guarantees},
  year={2019}
}

An Adjusted Nearest Neighbor Algorithm Maximizing the F-measure from Imbalanced Data

Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Stéphane Riou, Marc Sebban

2019

- ICTAI (International Conference on Tools with Artificial Intelligence)

[bibtex] (click to show)

@inproceedings{metzler2019gnn,
  title={An Adjusted Nearest Neighbor Algorithm Maximizing the F-measure from Imbalanced Data},
  author={ and Emonet, R{\'e}mi and Fromont, Elisa},
  booktitle={IEEE International Conference on Image Processing},
  year={2019}
}

From Cost-Sensitive Classification to Tight F-measure Bounds

Kevin Bascol, Rémi Emonet, Élisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban

2019

- AISTATS

[bibtex] (click to show)

@inproceedings{bascolmetzlerfmeasure,
  title={From Cost-Sensitive Classification to Tight F-measure Bounds},
  author={Bascol, Kevin and Emonet, Rémi and Fromont, Elisa and Habrard, Amaury and Metzler, Guillaume and Sebban, Marc},
  booktitle={AISTATS 2019},
  year={2019}
}

Year 2018

Fast and Provably Effective Multi-view Classification with Landmark-based SVM

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

2018

- ECML-PKDD

https://github.com/vzantedeschi/multiviewLSVM

[bibtex] (click to show)

@inproceedings{zantedeschi2018metric,
  title={ Fast and Provably Effective Multi-view Classification with Landmark-based SVM },
  author={Zantedeschi, Valentina and Emonet, R{\'e}mi and Sebban, Marc},
  booktitle={ECML-PKDD 2018},
  year={2018}
}

Year 2017

Unsupervised Domain Adaptation Based on Subspace Alignment

Basura Fernando, Rahaf Aljundi, Rémi Emonet, Amaury Habrard, Marc Sebban, Tinne Tuytelaars

2017

- Domain Adaptation in Computer Vision Applications

[bibtex] (click to show)

@incollection{fernando2017unsupervised,
  title={Unsupervised Domain Adaptation Based on Subspace Alignment},
  author={Fernando, Basura and Aljundi, Rahaf and Emonet, R{\'e}mi and Habrard, Amaury and Sebban, Marc and Tuytelaars, Tinne},
  booktitle={Domain Adaptation in Computer Vision Applications},
  pages={81--94},
  year={2017},
  publisher={Springer}
}
@book{csurka2017domain,
  title={Domain Adaptation in Computer Vision Applications},
  author={Csurka, Gabriela},
  year={2017},
  publisher={Springer},
  ISBN={978-3-319-58346-4},
  DOI={10.1007/978-3-319-58347-1},
  URL={http://www.springer.com/gp/book/9783319583464}
}

Residual Conv-Deconv Grid Network for Semantic Segmentation

Damien Fourure, Rémi Emonet, Élisa Fromont, Damien Muselet, Alain Trémeau, Christian Wolf

2017

- BMVC (British Machine Vision Conference)

https://github.com/Fourure/GridNet

[bibtex] (click to show)

@inproceedings{fourure2017gridnet,
  title={Residual Conv-Deconv Grid Network for Semantic Segmentation},
  author={Fourure, Damien and Emonet, R{\'e}mi and Fromont, Elisa and Muselet, Damien and Tr{\'e}meau, Alain and Wolf, Christian},
  booktitle={Proceedings of the British Machine Vision Conference, 2017},
  year={2017}
}

Multi-task, Multi-domain Learning: application to semantic segmentation and pose regression

Damien Fourure, Rémi Emonet, Élisa Fromont, Damien Muselet, Natalia Neverova, Alain Trémeau, Christian Wolf

2017

- Neurocomputing

[bibtex] (click to show)

@article{fourure2017multi,
  title={Multi-task, Multi-domain Learning: application to semantic segmentation and pose regression},
  author={Fourure, Damien and Emonet, R{\'e}mi and Fromont, Elisa and Muselet, Damien and Neverova, Natalia and Tr{\'e}meau, Alain and Wolf, Christian},
  journal={Neurocomputing},
  volume={251},
  pages={68--80},
  year={2017},
  publisher={Elsevier}
}

Year 2016
C

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

Valentina Zantedeschi, Rémi Emonet, Marc Sebban

2016

- NIPS (Neural Information Processing Systems)
http://vzantedeschi.com/betarisk.html
[bibtex] (click to show)
```
@inproceedings{zantedeschi2016beta,
  title={beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data},
  author={Zantedeschi, Valentina and Emonet, R{\'e}mi and Sebban, Marc},
  booktitle={NIPS 2016},
  year={2016}
}
```

Year 2015

Landmarks-based Kernelized Subspace Alignment for Unsupervised Domain Adaptation

Rahaf Aljundi, Rémi Emonet, Damien Muselet, Marc Sebban

2015

- CVPR (IEEE conf. Computer Vision and Pattern Recognition)

[bibtex] (click to show)

@inproceedings{aljundi2015landmarks,
  title={Landmarks-based Kernelized Subspace Alignment for Unsupervised Domain Adaptation},
  author={Aljundi, Rahaf and Emonet, R{\'e}mi and Muselet, Damien and Sebban, Marc},
  booktitle={Computer Vision and Pattern Recognition},
  year={2015}
}

Year 2014

Temporal Analysis of Motif Mixtures using Dirichlet Processes

Rémi Emonet, Jagannadan Varadarajan, Jean-Marc Odobez

2014

- TPAMI (Transactions on Pattern Analysis and Machine Intelligence)

[bibtex] (click to show)

@ARTICLE{Emonet_PAMI_2014,
         author = {Emonet, Remi and Varadarajan, Jagannadan and Odobez, Jean-Marc},
          title = {Temporal Analysis of Motif Mixtures using Dirichlet Processes},
        journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
           year = {2014}
}

Sparsity in Topic Models

Jagannadan Varadarajan, Rémi Emonet, Jean-Marc Odobez

2014

- Practical Applications of Sparse Modeling: Biology, Signal Processing and Beyond

[bibtex] (click to show)

@INCOLLECTION {Varadarajan_MITPRESS_2012,
    title = {Sparsity in Topic Models},
   author = {Varadarajan, Jagannadan and Emonet, Remi and Odobez, Jean-Marc},
booktitle = {Practical Applications of Sparse Modeling (Neural Information Processing series)},
     year = {2014},
publisher = {MIT Press},
      pdf = {https://publidiap.idiap.ch/downloads//papers/2012/Varadarajan_MITPRESS_2012.pdf},
}
@book{9780262027724,
  Author = {},
  Title = {Practical Applications of Sparse Modeling (Neural Information Processing series)},
  Publisher = {The MIT Press},
  Year = {2014},
  ISBN = {0262027720},
  URL = {http://www.amazon.com/Practical-Applications-Modeling-Information-Processing/dp/0262027720%3FSubscriptionId%3D0JYN1NVW651KCA56C102%26tag%3Dtechkie-20%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0262027720}
}

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Demos

For now, visit my videos page.

Presentations (research and related)

Here are presentations that I made publicly enough so that it makes sense to share them. (click the icon on the left to see the sources and a PDF export)