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Criteo AI Lab > Research blog

18 Sep 2020

Alex Gilotte, researcher at the Criteo AI Lab, details how counterfactual reasoning may be applied to evaluate a recommender system. The first posts introduce the topic and the intuitions behind the mathematics of counterfactual reasoning, and the later...

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08 Sep 2020

What defines large-scale machine learning? This seemingly innocent question is often answered with petabytes of data and hundreds of GPUs. It turns out that large-scale machine learning does not have much to do with all of that. In...

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02 Sep 2020

The paper on “A Principle of Least Action for the Training of Neural Networks” by Skander Karkar, Patrick Gallinari and their LIP6_lab co-authors, has been selected as the best student ML paper award Runner-up @ECMLPKDD!  https://bit.ly/3gL19qA

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06 Aug 2020

The Reco team has released DeepR, a package to train deep recommendation models in production! See the full post here, with links to Github, docs, quick start guide and some examples: DeepR on Medium.

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03 Jul 2020

One paper accepted at ECCV2020! “Do not mask what you do not need to mask: a parser free Virtual Try-on” Authors: T. Issenhuth (Criteo AI Lab), J. Mary (Criteo AI Lab), C. Calauzènes (Criteo AI Lab)

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10 Jun 2020

One paper accepted at ECML 2020: A Principle of Least Action for the Training of Neural Networks Authors. Skander Karkar (LIP6, Sorbonne Université / Criteo AI Lab), Ibrahim Ayed (LIP6, Sorbonne Université), Emmanuel de Bézenac (LIP6, Sorbonne Université),...

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01 Jun 2020

We are happy to have 9 papers co-authored by members of the Criteo AI Lab getting in ICML 2020: Gradient-free Online Learning in Continuous Games with Delayed Rewards,  A. Héliou, P. Mertikopoulos, and Z. Zhou. Finite-Time Last-Iterate Convergence...

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26 May 2020

Two papers co-authored by Vianney Perchet, researcher at Criteo AI Lab, got accepted at COLT 2020 Selfish Robustness and Equilibria in Multi-Player Bandits, E. Boursier, V. Perchet. https://arxiv.org/abs/2002.01197 Covariance-adapting algorithm for semi-bandits with application to sparse rewards, P....

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16 May 2020

Two papers co-authored by Criteo AI Lab researchers and their colleagues accepted at KDD 2020! Paper #1: Joint Policy-Value Learning for Recommendation Authors: Olivier Jeunen(intern), David Rohde, Flavian Vasile. Martin Bompaire Abstract. Conventional approaches to recommendation often do...

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17 Mar 2020

Our REVEAL workshop about Bandit and RL for user interactions will be held at RecSys’20. Co-organizers: Maria Dimakopoulou (Netflix), Thorsten Joachims (Cornell), Olivier Koch (Criteo AI Lab), Yves Raimond (Netflix)Adith Swaminathan (Microsoft), and Flavian Vasile (Crite AI Lab)....

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