Machine Learning - Optimization Research - NAVER LABS Europe


Innovative models to design algorithms and imagine new tasks that push the boundaries and bring to life intelligent systems in our everyday lives.

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During the last two decades, research in machine learning has evolved from the status of promising science to industrial reality. Formalized as an optimization task under constraint or as a mathematical integration, solutions to problems now exist that were previously considered beyond reach. In this context, the disciplines of machine learning and optimization constitute a cornerstone of the conception and development of systems with the capabilities to adapt and enhance through time.

We propose innovative models to design algorithms and imagine new tasks that push the possibilities given by this incoming revolution. These will make our vision a reality by bringing to life intelligent systems to supervise, enhance, secure and automate our everyday activities.

We work across deep learning, autonomous indoor robotics, adversarial learning protocols, machine reading and optimization in large graphs. We contribute to the development of the cutting edge products of NAVER LABS and are very active in the scientific community where we produce papers, contribute code and datasets and organise conferences, workshops and challenges.

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Our navigation system enables robots to adapt to specific real-world environments and use cases with only small amounts of human preference data. Blog article by Christopher Dance
JulienPerez Podcast
Robot learning workshop at NeurIPS2020 – podcast with Julien Perez

Recent publications

Machine Learning and Optimization team:

Theo Cachet
Darko Drakulic
Quentin Grail
Seungsu Kim
Sofia Michel
Michael Niemaz
Pankaj Pansari
Julien Perez
Group lead
Tomi Silander
Arnaud Sors