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April 30, 2021 Feynman Liang, Nimar Arora, Nazanin Tehrani, Yucen Li, Michael Tingley, Erik Meijer
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Accelerating Metropolis-Hastings with Lightweight Inference Compilation

In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in a framework we call Lightweight Inference Compilation (LIC). LIC implements amortized inference within an open-universe declarative probabilistic programming language (PPL).
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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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March 8, 2021 Fernanda de Lima Alcantara
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Diverse and inclusive representation in online advertising: An exploration of the current landscape and people’s expectations

Although there have been numerous studies about underrepresentation and misrepresentation of people in advertising, most have focused on traditional channels such as television, print, and radio, rather than on digital channels. In this paper, we seek to contribute to the body of knowledge by utilizing a mix of quantitative and qualitative methods to explore people’s attitudes toward diversity in online advertising, the current state of representation, and the impact of diversity on digital campaign performance.
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February 19, 2021 Eugenia Giraudy, Paige Maas, Shankar Iyer, Zack Almquist, JW Schneider, Alex Dow
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Measuring Long-Term Displacement Using Facebook Data

In this paper, we present a novel approach for using aggregated and anonymized Facebook location data to measure displacement patterns in the weeks and months after disasters.
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February 11, 2021 Carlos A. Gómez-Uribe, Brian Karrer
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The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models

Motivated by the needs of online large-scale recommender systems, we specialize the decoupled extended Kalman filter (DEKF) to factorization models, including factorization machines, matrix and tensor factorization, and illustrate the effectiveness of the approach through numerical experiments on synthetic and on real-world data.
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December 28, 2020 Michael Bailey, Drew Johnston, Martin Koenen, Theresa Kuchler, Dominic Russel, Johannes Stroebel
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Social Distancing During a Pandemic: The Role of Friends

We explore how social network exposure to COVID-19 cases shapes individuals’ social distancing behavior during the early months of the ongoing pandemic. We work with de-identified data from Facebook to show that U.S. users whose friends live in areas with worse coronavirus outbreaks reduce their mobility more than otherwise similar users whose friends live in areas with smaller outbreaks.
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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 10, 2020 Samuel Daulton, Maximilian Balandat, Eytan Bakshy
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Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization

Our empirical evaluation demonstrates that qEHVI is computationally tractable in many practical scenarios and outperforms state-of-the-art multi-objective BO algorithms at a fraction of their wall time.
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December 6, 2020 Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy
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High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization

We develop effective models that leverage the structure of the search space to enable contextual policy optimization directly from the aggregate rewards using Bayesian optimization.
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November 20, 2020 Mahnush Movahedi, Benjamin M. Case, Andrew Knox, Li Li, Yiming Paul Li, Sanjay Saravanan, Shubho Sengupta, Erik Taubeneck
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Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment (Extended Abstract)

We outline a way to deploy a privacy-preserving protocol for multiparty Randomized Controlled Trials on the scale of 500 million rows of data and more than a billion gates.
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