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)

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),...

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...

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....

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...

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)....

Our workshop will be on either July 17 or 18, and either in Vienna or virtual, depending on the decision of the conference organizers based on the state of the COVID-19 outbreak. The other workshop co-organizers are Michal...

3 papers at ICLR’20 workshop “Causal learning for decision making”: Optimization of Treatment Assignment with Generalization Guarantees – Artem Betlei, Eustache Diemert, Massih-Reza AminiIndividual Treatment Effect Estimation in Presence of Observable Interference – Thibaud Rahier, Amélie Héliou, Matthieu...

This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem. Tapping into a rich existing literature, we show that DRO is a principled tool for counterfactual decision making. We also...

tf-yarn is a Python library we have built at Criteo for training TensorFlow models on a YARN cluster. It supports running on one worker or on multiple workers with different distribution strategies, it can run on CPUs or GPUs and...