Marco Cuturi

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31 Posts
ML researcher. Ever feel the urge to compute a Wasserstein distance? check OTT-JAX! (https://ott-jax.readthedocs.io/)
Websitehttps://marcocuturi.net
G Scholarhttps://scholar.google.com/citations?user=kQEydDMAAAAJ

The Machine Learning Research (MLR) team @ Apple, has an open position for a research software engineer (RSWE) in Paris.

Please apply here:
https://jobs.apple.com/en-us/details/200517435/aiml-machine-learning-engineer-mlr?team=MLAI

RSWEs stands at the core of what we do @ MLR. If you have any questions on fit / research topics, please reach out to [email protected] .

AIML - Machine Learning Engineer, MLR - Careers at Apple

Apply for a AIML - Machine Learning Engineer, MLR job at Apple. Read about the role and find out if it’s right for you.

🍏The Apple MLR team in Paris has an open AI resident position (July 2024 to July 2025). See the link below for more info and application procedure. Please reach out to [email protected] if you
have questions! https://jobs.apple.com/en-us/details/200514644/aiml-resident-machine-learning-research?team=MLAI
AIML Resident - Machine Learning Research - Careers at Apple

Apply for a AIML Resident - Machine Learning Research job at Apple. Read about the role and find out if it’s right for you.

Apple MLR has a few intern positions in Paris. They can start pretty much anytime now, and last for up to a year (pending a finish before Sept. 24).

You must be a Phd student, have published in ML (e.g. Neurips/ICML/ICLR/AISTATS etc...). Topics of interest include differentiable optimization, generative models and uncertainty quantification.

Please reach out by email to [email protected] , list CV + github + whatever relevant details if you are interested!

Can you imagine a better place to learn about the ML than the MLSS '24@ Okinawa?? https://groups.oist.jp/mlss

Please share this info and apply!!

The Machine Learning Summer School in Okinawa 2024

March/04 (Mon) - March/15 (Fri), 2024, OIST conference center by the Okinawa Institute of Science and Technology (OIST) and RIKEN AIP. News - Submission site is now open! The machine learning summer school (MLSS) series was started in 2002 with the motivation to promulgate modern methods of statistical machine learning and inference.

OIST Groups

Can you imagine a better place to learn about the ML than the MLSS '24@ Okinawa?? https://groups.oist.jp/mlss

Please share this info and apply!!

The Machine Learning Summer School in Okinawa 2024

March/04 (Mon) - March/15 (Fri), 2024, OIST conference center by the Okinawa Institute of Science and Technology (OIST) and RIKEN AIP. News - Submission site is now open! The machine learning summer school (MLSS) series was started in 2002 with the motivation to promulgate modern methods of statistical machine learning and inference.

OIST Groups
You can download the slides here:
https://icml.cc/virtual/2023/tutorial/21559
Optimal Transport in Learning, Control, and Dynamical Systems Tutorial

I am at #ICML2023, where our tutorial on OT is about to finish.

I will be presenting two posters tomorrow, "Monge Bregman Occam..." and "The Monge Gap ...", I am looking forward to meeting you at the conference!

Last week, we had a really fun hackathon for ott-jax (https://ott-jax.readthedocs.io/), a toolbox for optimal transport computations. This was in the Paris Apple office, and I had a blast working with so many great people! Can't thank enough everyone who participated, both onsite and online! Definitely looking forward to the next edition of this 😀 Thanks to @pablin for this great idea!
Optimal Transport Tools (OTT) — ott 0.4.1.dev18+g18ee960 documentation

Today, we open sourced Fortuna (https://github.com/awslabs/fortuna) a library for uncertainty quantification.
Deep neural networks are often overconfident and do not know what they don’t know. Quantifying the uncertainty in the predictions they make will help deploy deep learning more responsibly and more safely.
#responsibleAI #ConformalPrediction #BayesianInference #UncertaintyQuantification #deeplearning #opensource
GitHub - awslabs/fortuna: A Library for Uncertainty Quantification.

A Library for Uncertainty Quantification. Contribute to awslabs/fortuna development by creating an account on GitHub.

GitHub

Another day another launch 😬
I am super excited to announce that we open sourced Renate, a #ContinualLearning library to automatically retrain and retune deep neural networks.
#deeplearning #opensource #aws

https://github.com/awslabs/Renate

GitHub - awslabs/Renate: Library for automatic retraining and continual learning

Library for automatic retraining and continual learning - GitHub - awslabs/Renate: Library for automatic retraining and continual learning

GitHub