Criteoailab
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Agentic AI Symposium 2026
A science‑fueled, multidisciplinary approach to Agentic AI Authors: Jean-Yves Franceschi & Mariia Vladimirova At the Criteo AI Lab, we believe that making AI both trustworthy and useful requires a continuous and open dialogue between scientists, engineers, policymakers, regulators, and industry leaders. As AI systems evolve from passive predictors to agentic systems capable of planning, using tools,…
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Our Experience at ALT 2025
A Journey into Algorithmic Learning Theory Authors: Marc Abeille, Julien Zhou & Corentin Pla. The Algorithmic Learning Theory (ALT) 2025 conference, held in Milan at the end of February, was a significant event in the field of machine learning and theoretical computer science. This one-track conference brought together hundred researchers and practitioners to discuss the latest…
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Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
Novel differentially private model using gradient flows defined on an optimal transport metric. This Research Card introduces a novel, theoretically grounded method for differentially private generative modeling by leveraging a smooth mathematical process, achieving high-fidelity data generation with strong privacy guarantees and lower computational costs compared to traditional approaches. Title: Differentially Private Gradient Flow based…
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Sharing our highlights from RecSys 2024
The 18th edition of the ACM Conference on Recommender Systems (RecSys) took place from October 14th to October 18th 2024, in Bari, Italy. As the leading international conference on recommender systems, RecSys continues to serve as the premier platform for presenting cutting-edge research, systems, and techniques in the field. This year’s event brought together top…
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Research Card: Repeated Bidding with Dynamic Value
This Research Card aims to formulate an optimal bidding strategy for repeated auctions, considering how a buyer’s utility is affected by the time since their last purchase. It also explores the costs involved in implementing simpler shading policies. Title: Repeated Bidding with Dynamic Value Short Title: How to optimally account for the user fatigue in…
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FAIRPLAY: Research program to Optimize Fair & Private Interactions between ML Agents in AI Marketpla
How can we ensure that an advertisement or a job offer can be seen in a non-discriminatory way by the people they are displayed to? Inria, ENSAE (National School of Statistics and Economics, part of the Institut Polytechnique de Paris), and the Adtech leader Criteo founded the FAIRPLAY program in 2022. This groundbreaking initiative addresses…
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Research Card: Strategic Arms with Side Communication Prevail Over Low-Regret MAB Algorithms
Paper: Strategic Arms with Side Communication Prevail Over Low-Regret MAB AlgorithmsAuthors: Ahmed Ben Yahmed, Clément Calauzènes, Vianney PerchetCategory: Learning Theory, Sequential LearningRevue: ICASSP 2024 Why did we work on this topic (the problem we want to solve)? We chose to investigate this topic because we aimed to address a fundamental problem within the strategic multi-armed bandit…
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Research Card: Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous Scenes
Paper: Exploring 3D-aware Latent Spaces for Efficiently Learning Numerous ScenesAuthors : Antoine Schnepf (Criteo AI Lab), Karim Kassab (Criteo AI Lab), Jean-Yves Franceschi (Criteo AI Lab), Laurent Caraffa, Flavian Vasile (Criteo AI Lab), Jeremie Mary (Criteo AI Lab), Andrew Comport, Valérie Gouet-BrunetCategory: Deep Learning, Computer vision, 3DRevue: CVPR 2024 3DMV Workshop Why did we work on…
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Research Card: Federated Wasserstein Distance
How to tackle the challenge of calculating the similarity between two sets of embeddings, each held by a different party, while respecting privacy restrictions? Paper: Federated Wasserstein DistanceAuthors: Alain Rakotomamonjy (Criteo AI Lab), Kimia Nadjahi (MIT), Liva Ralaivola (Criteo AI Lab).Category: Federated Wasserstein DistanceRevue: ICLR 2024 Why did we work on this topic, the problem…
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Research Card: Unifying GANs and Score-Based Diffusion as Generative Particle Models
How to merge two different types of generative models into a single framework, allowing the discovery of new types of generative models via hybridization. Paper: Unifying GANs and Score-Based Diffusion as Generative Particle ModelsAuthors: Jean-Yves Franceschi (Criteo AI Lab), Mike Gartrell (*Criteo AI Lab), Ludovic Dos Santos (Criteo AI Lab), Thibaut Issenhuth (Criteo AI Lab…