Research
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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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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…
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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…
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Learning from Multiple Sources for Data-to-Text and Text-to-Data
How to generate fluent text from structured data and vice versa by leveraging heterogeneous data sources. Paper: https://arxiv.org/abs/2302.11269Category: Natural Language ProcessingRevue: AISTATS 2023 Why did we work on this topic (the problem we want to solve)? Data-to-text (D2T) and text-to-data (T2D) are dual tasks that convert structured data such as graphs or tables into fluent text, and…