World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
7502
World Ranking
5438
National Ranking
112

Overview

Michèle Sebag is affiliated with the University of Paris-Saclay in France and has a research focus predominantly in computer science, with a significant contribution to artificial intelligence. Their work spans various subfields including artificial intelligence, molecular biology, computer vision and pattern recognition, statistical and nonlinear physics, and computer networks and communications.

The scientist's research covers a range of topics, with notable emphasis on Bayesian modeling and causal inference, machine learning and data classification, neural networks and applications, generative adversarial networks and image synthesis, topic modeling, single-cell and spatial transcriptomics, and model reduction and neural networks.

Michèle Sebag has contributed to multiple publication venues, including.

  • arXiv (Cornell University)
  • AEA Randomized Controlled Trials
  • Frontiers in Big Data
  • Bioinformatics
  • IEEE Computer Graphics and Applications

Frequently collaborating with other researchers, Sebag's coauthors include:

  • Victor Berger
  • Shuyu Dong
  • Kento Uemura
  • Akito Fujii
  • Shuang Chang

Recent publications by Michèle Sebag comprise:

  • Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities, 2020, Frontiers in Big Data
  • GAN-based data augmentation for transcriptomics: survey and comparative assessment, 2023, Bioinformatics
  • Cartolabe: A Web-Based Scalable Visualization of Large Document Collections, 2020, IEEE Computer Graphics and Applications
  • Variational Auto-Encoder: not all failures are equal, 2020, arXiv (Cornell University)
  • In Silico Generation of Gene Expression profiles using Diffusion Models, 2024, bioRxiv (Cold Spring Harbor Laboratory)

In addition to journal and conference publications, Sebag has authored books published by the Centre National de la Recherche Scientifique, including "Actes de la conférence CAID 2020" released in 2021.

Best Publications

  • Extending Population-Based Incremental Learning to Continuous Search Spaces

    Michèle Sebag;Michèle Sebag;Antoine Ducoulombier;Antoine Ducoulombier

  • The grand challenge of computer Go: Monte Carlo tree search and extensions

    Sylvain Gelly;Levente Kocsis;Marc Schoenauer;Michèle Sebag

  • TreeFinder: a first step towards XML data mining

    A. Termier;M.-C. Rousset;M. Sebag

  • Xyleme, a dynamic warehouse for XML data of the Web

    S. Abiteboul;V. Aguilera;S. Ailleret;B. Amann

  • Adaptive operator selection with dynamic multi-armed bandits

    Luis DaCosta;Alvaro Fialho;Marc Schoenauer;Michèle Sebag

  • Analyzing bandit-based adaptive operator selection mechanisms

    Álvaro Fialho;Luis Da Costa;Marc Schoenauer;Michèle Sebag

  • BenchNN: On the broad potential application scope of hardware neural network accelerators

    Tianshi Chen;Yunji Chen;Marc Duranton;Qi Guo

  • APRIL: active preference learning-based reinforcement learning

    Riad Akrour;Marc Schoenauer;Michèle Sebag

  • Tractable induction and classification in first order logic via stochastic matching

    Michele Sebag;Celine Rouveirol

  • Extreme Value Based Adaptive Operator Selection

    Álvaro Fialho;Luís Costa;Marc Schoenauer;Michèle Sebag

  • Analysis of the AutoML Challenge Series 2015–2018

    Isabelle Guyon;Isabelle Guyon;Lisheng Sun-Hosoya;Lisheng Sun-Hosoya;Marc Boullé;Hugo Jair Escalante

  • Preference-based policy learning

    Riad Akrour;Marc Schoenauer;Michele Sebag

  • Data Streaming with Affinity Propagation

    Xiangliang Zhang;Cyril Furtlehner;Michèle Sebag

  • Comparison-based optimizers need comparison-based surrogates

    Ilya Loshchilov;Marc Schoenauer;Michèle Sebag

  • Data Stream Clustering With Affinity Propagation

    Xiangliang Zhang;Cyril Furtlehner;Cécile Germain-Renaud;Michèle Sebag

  • Multi-armed Bandit, Dynamic Environments and Meta-Bandits

    Cédric Hartland;Sylvain Gelly;Nicolas Baskiotis;Olivier Teytaud

  • Compact Unstructured Representations for Evolutionary Design

    Hatem Hamda;François Jouve;Evelyne Lutton;Marc Schoenauer

  • Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy

    Ilya Loshchilov;Marc Schoenauer;Michele Sebag

  • Feature Selection as a One-Player Game

    Romaric Gaudel;Michele Sebag

  • Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities.

    Remy Kusters;Dusan Misevic;Hugues Berry;Antoine Cully

  • Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits

    Nicolas Galichet;Michèle Sebag;Olivier Teytaud

Frequent Co-Authors

Marc Schoenauer
Marc Schoenauer French Institute for Research in Computer Science and Automation - INRIA
Xiangliang Zhang
Xiangliang Zhang University of Notre Dame
Sylvain Gelly
Sylvain Gelly Google (United States)
Isabelle Guyon
Isabelle Guyon University of Paris-Saclay
Francesco Bonchi
Francesco Bonchi Institute for Scientific Interchange
Aristides Gionis
Aristides Gionis Royal Institute of Technology
Elena Marchiori
Elena Marchiori Radboud University
A. E. Eiben
A. E. Eiben Vrije Universiteit Amsterdam
David Lopez-Paz
David Lopez-Paz Facebook AI Research (FAIR) in Paris
Alexander Statnikov
Alexander Statnikov Vanderbilt University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online education in Computer Science opens up a range of flexible and rewarding pathways. For those eager to enter the workforce quickly, you might consider the fastest online degree programs, which can lead to solid salaries in a short time. These are ideal for career changers or anyone seeking a quick boost.

Technology enthusiasts may find specialized degrees in AI especially attractive, as artificial intelligence continues to drive demand for skilled professionals. Carefully selecting your major is crucial; check out insights into the best majors for the strongest job prospects in the tech industry.

If you’re looking to enhance your credentials but worried about balancing work and study, researching the easy masters degrees available can help you find programs that suit your schedule and goals. Online options offer flexibility, affordability, and a stepping stone to various tech-driven careers in the USA.

Best Scientists Citing Michèle Sebag

Trending Scientists