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March 31, 2021 Christian Kroer, Nicolas E. Stier-Moses
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Market Equilibrium Models in Large-Scale Internet Markets

We focus on Internet advertising auctions, fair division problems, content recommendation systems, and robust abstractions of large-scale markets.
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December 23, 2020 Duncan C. McElfresh, Christian Kroer, Sergey Pupyrev, Eric Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P. Dickerson
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Matching Algorithms for Blood Donation

Using the recently deployed Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities. In both simulations and real experiments we match potential donors with opportunities, guided by a machine learning model trained on prior observations of donor behavior.
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December 16, 2020 Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti
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Online Bayesian Persuasion

In Bayesian persuasion, an informed sender has to design a signaling scheme that discloses the right amount of information so as to influence the behavior of a self-interested receiver. This kind of strategic interaction is ubiquitous in real-world economic scenarios. However, the seminal model by Kamenica and Gentzkow makes some stringent assumptions that limit its applicability in practice.
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December 7, 2020 Riccardo Colini Baldeschi, Julian Mestre, Okke Schrijvers, Christopher A. Wilkens
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The Ad Types Problem

In this paper we introduce the Ad Types Problem, a generalization of the traditional positional auction model for ad allocation that better captures some of the challenges that arise when ads of different types need to be interspersed within a user feed of organic content.
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October 9, 2020 Neta Barkay, Curtiss Cobb, Roee Eilat, Tal Galili, Daniel Haimovich, Sarah LaRocca, Katherine Morris, Tal Sarig
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Weights and Methodology Brief for the COVID-19 Symptom Survey by University of Maryland and Carnegie Mellon University, in Partnership with Facebook

The Facebook company is partnering with academic institutions to support COVID-19 research and to help inform public health decisions. Currently, we are inviting Facebook app users in the United States to take a survey collected by faculty at Carnegie Mellon University (CMU) Delphi Research Center, and we are inviting Facebook app users in more than 200 countries or territories globally to take a survey collected by faculty at the University of Maryland (UMD) Joint Program in Survey Methodology.
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September 30, 2020 Georgij Alekseev, Safaa Amer, Manasa Gopal, Theresa Kuchler, JW Schneider, Johannes Stroebel, Nils Wernerfelt
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The Effects of COVID-19 on U.S. Small Businesses: Evidence from Owners, Managers, and Employees

We analyze a large-scale survey of owners, managers, and employees of small businesses in the United States to understand the effects of the early stages of the COVID-19 pandemic on those businesses.
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August 22, 2020 Julian Mestre, Nicolas Stier
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Tight approximation for the minimum bottleneck generalized matching problem

We study a problem arising in statistical analysis called the minimum bottleneck generalized matching problem that involves breaking up a population into blocks in order to carry out generalizable statistical analyses of randomized experiments.
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July 20, 2020 Kevin Liou, Sean Taylor
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Variance-Weighted Estimators to Improve Sensitivity in Online Experiments

As companies increasingly rely on experiments to make product decisions, precisely measuring changes in key metrics is important. Various methods to increase sensitivity in experiments have been proposed, including methods that use pre-experiment data, machine learning, and more advanced experimental designs. However, prior work has not explored modeling heterogeneity in the variance of individual experimental users. We propose a more sensitive treatment effect estimator that relies on estimating the individual variances of experimental users using pre-experiment data.
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July 1, 2020 Zhixiang Gu, Jose Moreira, David Edelsohn, Ariful Azad
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Bandwidth-Optimized Parallel Algorithms for Sparse Matrix-Matrix Multiplication using Propagation Blocking

Sparse matrix-matrix multiplication (SpGEMM) is a widely used kernel in various graph, scientific computing and machine learning algorithms. It is well known that SpGEMM is a memory-bound operation, and its peak performance is expected to be bound by the memory bandwidth. Yet, existing algorithms fail to saturate the memory bandwidth, resulting in suboptimal performance under the Roofline model. In this paper, we characterize existing SpGEMM algorithms based on their memory access patterns and develop practical lower and upper bounds for SpGEMM performance.
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June 9, 2020 Julian Runge, Steve Geinitz, Simon Ejdemyr
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Experimentation and Performance in Advertising: An Observational Survey of Firm Practices on Facebook

It is widely assumed that firms experiment with their online advertising to identify more profitable approaches to then increase their investment in more profitable advertising, increasing their overall performance. Generalizable evidence on the actual use of such experiment-based learning by firms is sparse. The study herein addresses this shortcoming – detailing the extent to which large advertisers are utilizing experimentation along with evidence on the benefits of doing so.
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