Maria-Irina Nicolae
Researcher in Machine Learning at Bosch Center for Artificial Intelligence
Robert Bosch Campus 1
71272 Renningen
Email: irina(dot)nicolae(at)bosch(dot)com
Research Interests
- Intrusion detection, adversarial machine learning and model security
- Time series prediction
- Metric and similarity learning
- Statistical learning theory
Short bio
I am a machine learning scientist and consultant at Bosch Center for Artificial Intelligence.
Previously, I worked as a research scientist in the AI & Machine Learning team at IBM Research.
I obtained my Ph.D. from the University of Saint-Etienne in 2016 under the supervision of
Marc Sebban and
Éric Gaussier.
I graduated from
Politehnica University of Bucharest in Computer Science in 2011, and from
ENSIMAG in Information Systems in 2013.
Talks
Publications
[Google Scholar profile] [dblp profile]
2018
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F. Pollok, S. Boag, M.-I. Nicolae
Open Fabric for Deep Learning Models. MLOSS Workshop: Sustainable Communities at NeurIPS, 2018.
[PDF]
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M.-I. Nicolae, M. Sinn, M. N. Tran, A. Rawat, M. Wistuba, V. Zantedeschi, I. Molloy, B. Edwards
Adversarial Robustness Toolbox.
[PDF]
2017
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A. Rawat, M. Wistuba, M.-I. Nicolae
Harnessing Model Uncertainty for Detecting Adversarial Examples. NIPS Workshop on Bayesian Deep Learning, 2017.
[PDF][arXiv version]
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V. Lonij, A. Rawat, M.-I. Nicolae
Extending Knowledge Bases Using Images. NIPS Workshop on Automated Knowledge Base Construction, 2017.
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V. Zantedeschi, M.-I. Nicolae, A. Rawat
Efficient Defenses Against Adversarial Attacks. ACM CCS Workshop on Artificial Intelligence and Security (AISec), 2017.
[arXiv version]
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V. Lonij, A. Rawat, M.-I. Nicolae
Open-World Visual Recognition Using Knowledge Graphs, arXiv:1708.08310, 2017.
[PDF]
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M. Sinn, A. Rawat, M.-I. Nicolae
Rigorous Analysis of Adversarial Training with Empirical Distributions. ICML Workshop on Implicit Generative Modeling, 2017.
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M. Sinn, A. Rawat, M.-I. Nicolae
Practical Adversarial Training with Empirical Distributions. ICML Workshop on Implicit Generative Modeling, 2017.
2016
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M.-I. Nicolae
Learning Similarities for Linear Classification: Theoretical Foundations and Algorithms. Ph.D. thesis, Université Jean Monnet de Saint-Étienne, 2016.
[PDF]
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M.-I. Nicolae, E. Gaussier, A. Habrard, M. Sebban
Similarity Learning for Time Series Classification. arXiv:1610.04783, 2016.
[PDF]
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M.-I. Nicolae, E. Gaussier, A. Habrard, M. Sebban
Apprentissage de Similarités pour la Classification de Séries Temporelles Multivariées. French Conference on Machine Learning (CAp'16), 2016.
2015
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M.-I. Nicolae, M. Sebban, A. Habrard, E. Gaussier, M.-R. Amini
Algorithmic Robustness for Semi-Supervised (ε, γ, τ)-Good Metric Learning. ICONIP, 2015.
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M.-I. Nicolae, M. Sebban, A. Habrard, E. Gaussier
Joint Semi-Supervised Similarity Learning for Linear Classification. ECML/PKDD, 2015.
[PDF] [slides] [poster] [bibtex]
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M.-I. Nicolae, M. Sebban, A. Habrard, E. Gaussier
Apprentissage Joint Semi-Supervisé de Bonnes Similarités. French Conference on Machine Learning (CAp'15), 2015.
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M.-I. Nicolae, M. Sebban, A. Habrard, E. Gaussier, M.-R. Amini
Algorithmic Robustness for Learning via (ε, γ, τ)-Good Similarity Functions. ICLR Workshop, 2015.
[PDF] [bibtex]