Research
What Stood Out at ICLR 2026: Criteo Papers and Research Highlights
Explore Criteo’s ICLR 2026 research highlights, from contextual bandits and data valuation to 3D vision, diffusion models, and agentic AI.
The Grind Behind the Epiphany: A Short Story of a Research Project
Follow Criteo’s research journey on Learning from Label Proportions, from early experiments and rejection to a simpler, more rigorous algorithm.
On the Impact of the Utility in Semivalue-based Data Valuation
A new diagnostic method that scores data valuation robustness for cleaning, efficient training, and fair partner compensation. This Research Card introduces a new way to measure how trustworthy data valuation really is, by checking how much rankings of “most valuable data” change when the performance metric changes. Using a geometric representation of data contributions, the…
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,…
Off-Policy Learning in Large Action Spaces
Optimization Matters More Than Estimation This Research Card shows that in large-scale recommendation systems, like those used at Criteo, focusing on how we optimize algorithms is more impactful than trying to perfectly estimate outcomes. By using simpler, more stable training objectives, we can achieve better click-through rates, faster experimentation, and more scalable performance across millions…
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…
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,…
