ICML, the International Conference on Machine Learning, is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning.

ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, or robotics.

ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

This year, it was held in Baltimore, Maryland USA, from July 17 to 23. The DSAIDIS researchers presented three papers:

Functional Output Regression with Infimal Convolution: Exploring the Huber and ϵϵ-insensitive Losses [Arxiv]
Alex Lambert (KU Leuven) · Dimitri Bouche (Télécom Paris) · Zoltan Szabo (Ecole Polytechnique) · Florence d’Alché-Buc (Télécom Paris, Institut Polytechnique de Paris)

Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters [Arxiv]
Luc Brogat-Motte (Télécom Paris) · Rémi Flamary (École Polytechnique) · Celine Brouard (INRAE) · Juho Rousu (Aalto University) · Florence d’Alché-Buc (Télécom Paris, Institut Polytechnique de Paris)

Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model [Arxiv]

Jean-Rémy Conti (Télécom Paris Idemia) · Nathan NOIRY (Telecom Paris) · Vincent Despiegel (Idemia) · Stéphane Gentric (IDEMIA) · Stephan Clemencon (Telecom ParisTech)

2022|The Thirty-ninth International Conference on Machine Learning.