Research
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The Grind Behind the Epiphany: A Short Story of a Research Project
From ideation to outcome, this is the story of a privacy-preserving research project. It tells how research can generate innovations but also joy and despair. Early 2024: The “Hammer” Phase Two research leads who pioneered Criteo’s early privacy initiatives, as part of the Criteo multi-year research program, introduced me to a challenge born from the Privacy…
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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…
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Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Search
A rule for choosing when to sell/buy an asset when facing a sequence of prices This Research Card explains a rule for choosing when to sell/buy an asset when facing a sequence of prices. The key feature is that the algorithm has access to “predictions” on the optimal price. The objectives are to take “safe” decisions,…
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Improving Consistency Models with Generator-Augmented Flows
Delivering more personalized ads in real-time — boosting engagement and cutting infrastructure costs. This Research Card presents Generator-Augmented Flows (GC), a technique improving consistency models, which are state-of-the-art models for fast image and video generation. For Criteo, this could mean delivering more personalized ads in real time — boosting engagement and cutting infrastructure costs. Title: Improving Consistency Models with…
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Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder
A novel IG-AE that creates a 3D-aware latent space compatible with Neural Radiance Fields This Research Card introduces a novel Inverse Graphics Autoencoder (IG-AE) that creates a 3D-aware latent space compatible with Neural Radiance Fields (NeRFs), enabling efficient 3D object reconstruction from 2D images and facilitating high-quality 3D ads. Title: Bringing NeRFs to the Latent Space:…
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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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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…