Bidding Agent Design in the LinkedIn Ad Marketplace

no code implementations • 25 Feb 2022 • Yuan Gao, Kaiyu Yang, Yuanlong Chen, Min Liu, Noureddine El Karoui

We establish a general optimization framework for the design of automated bidding agent in dynamic online marketplaces.

A Framework for Fairness in Two-Sided Marketplaces

no code implementations • 23 Jun 2020 • Kinjal Basu, Cyrus DiCiccio, Heloise Logan, Noureddine El Karoui

Incorporating fairness while building such systems is crucial and can have a deep social and economic impact (applications include job recommendations, recruiters searching for candidates, etc.).

Fairness Recommendation Systems +1

Achieving Fairness via Post-Processing in Web-Scale Recommender Systems

no code implementations • 19 Jun 2020 • Preetam Nandy, Cyrus DiCiccio, Divya Venugopalan, Heloise Logan, Kinjal Basu, Noureddine El Karoui

Building fair recommender systems is a challenging and crucial area of study due to its immense impact on society.

Attribute Fairness +2

Can we trust the bootstrap in high-dimension?

no code implementations • 2 Aug 2016 • Noureddine El Karoui, Elizabeth Purdom

We consider ordinary least-squares as well as robust regression methods and adopt a minimalist performance requirement: can the bootstrap give us good confidence intervals for a single coordinate of $\beta$?

regression Vocal Bursts Intensity Prediction

Connection graph Laplacian methods can be made robust to noise

no code implementations • 23 May 2014 • Noureddine El Karoui, Hau-Tieng Wu

Recently, several data analytic techniques based on connection graph laplacian (CGL) ideas have appeared in the literature.

Graph connection Laplacian and random matrices with random blocks

no code implementations • 1 Oct 2013 • Noureddine El Karoui, Hau-Tieng Wu

Graph connection Laplacian (GCL) is a modern data analysis technique that is starting to be applied for the analysis of high dimensional and massive datasets.