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July 1, 2021 Vincent Conitzer, Christian Kroer, Eric Sodomka, Nicolas E. Stier-Moses
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Multiplicative Pacing Equilibria in Auction Markets

Motivated by a mechanism used in practice by several companies, this paper considers a smoothing procedure that relies on pacing multipliers: on behalf of each buyer, the auction market applies a factor between 0 and 1 that uniformly scales the bids across all auctions.
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April 21, 2021 Vashist Avadhanula, Riccardo Colini Baldeschi, Stefano Leonardi, Karthik Abinav Sankararaman, Okke Schrijvers
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Stochastic bandits for multi-platform budget optimization in online advertising

We study the problem of an online advertising system that wants to optimally spend an advertiser’s given budget for a campaign across multiple platforms, without knowing the value for showing an ad to the users on those platforms.
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April 13, 2021 Deeksha Sinha, Karthik Abinav Sankararaman, Abbas Kazerouni, Vashist Avadhanula
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Multi-armed Bandits with Cost Subsidy

In this paper, we consider a novel variant of the multi-armed bandit (MAB) problem, MAB with cost subsidy, which models many real-life applications where the learning agent has to pay to select an arm and is concerned about optimizing cumulative costs and rewards.
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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 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 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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December 7, 2020 Max Balandat, Brian Karrer, Daniel Jiang, Sam Daulton, Ben Letham, Andrew Gordon Wilson, Eytan Bakshy
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BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization

We introduce BOTORCH, a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions, a novel sample average approximation optimization approach, auto-differentiation, and variance reduction techniques.
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December 7, 2020 Shali Jiang, Daniel Jiang, Max Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
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Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree.
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October 17, 2020 Justin Cheng, Moira Burke, Bethany de Gant
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Country Differences in Social Comparison on Social Media

Social comparison is a common focus in discussions of online social media use and differences in its frequency, causes, and outcomes may arise from country or cultural differences. To understand how these differences play a role in experiences of social comparison on Facebook, a survey of 37,729 people across 18 countries was paired with respondents’ activity on Facebook.
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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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