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3 papers at ICLR’20 workshop “Causal learning for decision making”

3 papers at ICLR’20 workshop “Causal learning for decision making”: Optimization of Treatment Assignment with…

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“Distributionally Robust Counterfactual Risk Minimization” paper at AAAI 2020

This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk…

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Train TensorFlow models on YARN in just a few lines of code!

tf-yarn is a Python library we have built at Criteo for training TensorFlow models on a…

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Highlights of our 5 papers accepted to ICML 2019

It’s been a year since we launched our AI Lab to drive and shape innovation…

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Highlights of ScaledML 2019

Five Criteos attended on March 27th & 28th the conference ScaledML 2019 at the Computer History Museum in Mountain…

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Hyper-parameter optimization algorithms: a short review

At Criteo AI Lab, we are using more and more complex machine learning (ML) models…

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Key takeaways from AAAI 2019

We were there for the 2019 edition of AAAI that took place in Honolulu, Hawaii…

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Criteo crashcourse on TensorFlow with Aurélien Géron

As part of our Outreach Program, our AI Lab proud to give to the Machine…

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Learnings from the 2018 French Statistics Week on the concept of “cause”

Introduction  Every two years, the French Society of Statistiques (SFdS) organizes a conference on a statistical topic…

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On Nearest Neighbors and Hubs in High-Dimensional Data

Context As hype is mounting around deep learning, do we still need to care about…

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