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).
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.
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.
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.
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.
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.