Research
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R&D Community Awards 2024
Another year has passed, and we came together to celebrate our accomplishments and honor the Criteo R&D Ambassadors who contributed to our success. At Criteo R&D, we are a welcoming community of experts connected by our passion for tech. Sharing knowledge helps us build bigger and better together, which is the primary motivation for the R&D…
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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: Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection, and Learning
We discuss a new method for off-policy evaluation, selection, and learning in interactive systems. Title: Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection, and Learning Short Title: A New Principled Method for Improved Evaluation, Selection, and Learning in Interactive Systems Authors: Otmane Sakhi (Criteo AI Lab, France), Imad Aouali (CREST, ENSAE; Criteo AI Lab, France), Pierre…
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Research Card: FairJob – A Real-World Dataset for Fairness in Online Systems
Research Card on how to make the outcome prediction in online systems fairer? Title: FairJob: A Real-World Dataset for Fairness in Online Systems Short Title: Discover and mitigate bias in advertising Authors: Mariia Vladimirova (Criteo AI Lab), Federico Pavone (Paris-Dauphine), Eustache Diemert (Criteo AI Lab) Team: Research.FDL, collaboration with Paris-Dauphine University Revue: NeurIPS 2024, datasets and…
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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: Mode Identification with Partial Feedback
An adaptive algorithm that learns the best sequence of questions to identify as quickly as possible (in terms of questions) the most probable label for data points. Paper: Mode Identification with Partial Feedback Authors: Vivien Cabannes (Meta), Charles Arnal (Univ. Paris Saclay), V. Perchet (Ensae & Criteo) Category: Annotation Revue: COLT 2024 Why did we work…
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Attending to JEP-TALN RECITAL 2024
JEP-TALN RECITAL is the main gathering of French-speaking NLP researchers. It combines three historical French conferences. Journées d’Etudes sur la Parole: centered on speech. Traitement Automatique de la Langue Naturelle: centered on text. Rencontres des Etudiants Chercheurs en Informatique et Traitement Automatique des Langues: a space for students to showcase their work. We attended to…
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Research Card: Fixed Point Label Attribution for Real-Time Bidding
Research Card on how to attribute positive outcomes to touchpoints in a sequence of displays leading to a conversion. Paper: Fixed Point Label Attribution for Real-Time BiddingAuthors: Martin Bompaire, Antoine Désir, Benjamin HeymannCategory: Operations ResearchRevue: Manufacturing and Service Operations Management Why did we work on this topic (the problem we want to solve)? In display advertising,…
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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…