Research
Online Learning for Measuring Incentive Compatibility in Ad Auctions
May 14, 2019
Abstract
In this paper we investigate the problem of measuring end-to-end Incentive Compatibility (IC) regret given black-box access to an auction mechanism. Our goal is to 1) compute an estimate for IC regret in an auction, 2) provide a measure of certainty around the estimate of IC regret, and 3) minimize the time it takes to arrive at an accurate estimate. We consider two main problems, with different informational assumptions: In the advertiser problem the goal is to measure IC regret for some known valuation v, while in the more general demand-side platform (DSP) problem we wish to determine the worst-case IC regret over all possible valuations. The problems are naturally phrased in an online learning model and we design Regret-UCB algorithms for both problems. (Download for full abstract.)
Related Publications
June 11, 2025
Computer Vision
IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
Florian Bordes, Quentin Garrido, Justine Kao, Adina Williams, Mike Rabbat, Emmanuel Dupoux
June 11, 2025
June 10, 2025
Computer Vision
A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Benno Krojer, Mojtaba Komeili, Candace Ross, Quentin Garrido, Koustuv Sinha, Nicolas Ballas, Mido Assran
June 10, 2025
June 10, 2025
Robotics
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba Komeili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, Nicolas Ballas
June 10, 2025
May 14, 2025
Core Machine Learning
UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso-Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R. Kitchin, Daniel S. Levine, Kyle Michel, Anuroop Sriram, Taco Cohen, Abhishek Das, Ammar Rizvi, Sushree Jagriti Sahoo, Zachary W. Ulissi, C. Lawrence Zitnick
May 14, 2025
April 08, 2021
Responsible AI
Integrity
Towards measuring fairness in AI: the Casual Conversations dataset
Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, Cristian Canton Ferrer
April 08, 2021
April 30, 2018
The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings | Facebook AI Research
Tomer Galanti, Lior Wolf, Sagie Benaim
April 30, 2018
April 30, 2018
Computer Vision
NAM – Unsupervised Cross-Domain Image Mapping without Cycles or GANs | Facebook AI Research
Yedid Hoshen, Lior Wolf
April 30, 2018
December 11, 2019
Speech & Audio
Computer Vision
Hyper-Graph-Network Decoders for Block Codes | Facebook AI Research
Eliya Nachmani, Lior Wolf
December 11, 2019
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2025