Alice Schoenauer Sebag, PhD

The Multilingual Divide and Its Impact on Global AI Safety

A. Peppin, J. Kreutzer, A. Schoenauer Sebag, K. Marchisio, B. Ermis, J. Dang, S. Cahyawijaya, S. Singh, S. Goldfarb-Tarrant, V. Aryabumi, Aakanksha, W.-Y. Ko, A. Üstün, M. Gallé, M. Fadaee, S. Hooker

arXiv:2505.21344 (2025)

arXiv

Command A: An Enterprise-Ready Large Language Model

T. Cohere, A. Ahmadian, M. Ahmed, J. Alammar, M. Alizadeh, Y. Alnumay, ... A. Schoenauer Sebag, et al.

arXiv:2504.00698 (2025)

arXiv

Introducing v0.5 of the AI Safety Benchmark from MLCommons

B. Vidgen, A. Agrawal, A. M. Ahmed, ... A. Schoenauer Sebag, et al. (100+ authors)

arXiv:2404.12241 (2024)

arXiv

Assessing Online True Threats and Their Impacts: The New Standard of Counterman v. Colorado

J. Kovacs-Goodman, A. Schoenauer Sebag

Harvard Journal of Law & Technology (2023)

article | pdf

A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter

K. Yee, A. Schoenauer Sebag, O. Redfield, M. Eck, E. Sheng, L. Belli

Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP), ACL 2023

pdf | arXiv

Can we get smarter than majority vote? Efficient use of individual rater's labels for content moderation

C. Shin, A. Schoenauer Sebag

Efficient Natural Language and Speech Processing (ENLSP), NeurIPS Workshop (2022)

pdf

What 3.5 million French firms can tell us about the efficiency of Covid-19 support measures

B. Coeuré et al.

Committee on the monitoring and evaluation of financial support measures (2021)

French | English abstract

Multi-Domain Adversarial Learning

A. Schoenauer Sebag, L. Heinrich, M. Schoenauer, M. Sebag, L. F. Wu, S. J. Altschuler

International Conference on Learning Representations (ICLR 2019)

pdf | code

Stochastic Gradient Descent: Going As Fast As Possible But Not Faster

A. Schoenauer Sebag, M. Schoenauer, M. Sebag

OPT 2017: 10th NIPS Workshop on Optimization for Machine Learning

arXiv | code

The versatility of high-content high-throughput time-lapse screening data: developing generic methods for data re-use and comparative analyses

A. Schoenauer Sebag

PhD thesis, Mines ParisTech (2015)

pdf

A generic methodological framework for studying single cell motility in high-throughput time-lapse data

A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert, T. Walter

Bioinformatics, 31(12):i320-i328 (2015)

pdf | code

Infering an ontology of single cell motions from high-throughput microscopy data

A. Schoenauer Sebag, S. Plancade, C. Raulet-Tomkiewicz, R. Barouki, J.-P. Vert, T. Walter

Proceedings of the 12th IEEE International Symposium on Biomedical Imaging (ISBI):160-163 (2015)

pdf