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Introducing causal network motifs: A new approach to identifying heterogeneous spillover effects
This project is joint work with Yuan Yuan, PhD candidate at MIT, and the Facebook Core Data Science team. Learn…

News
Core Data Science researchers discuss research award opportunity in adaptive experimentation
On February 24, Facebook launched a request for proposals (RFP) on sample-efficient sequential Bayesian decision making, which closes on April…

Publication
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.
RFP
Request for proposals on sample-efficient sequential Bayesian decision making
With this RFP, we hope to deepen our ties to the academic research community by seeking out innovative ideas and applications of Bayesian optimization that further advance the field. We are committed to open source and will help awardees make the products of this RFP available to the public as part of BoTorch.
News
What precautions do people take for COVID-19?
To prevent the spread of COVID-19, people have been encouraged to adopt preventative measures such as hand-washing and mask-wearing. Through…

Publication
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.
Publication
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.
Publication
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.
Publication
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.
Publication
Envy, Regret, and Social Welfare Loss
In this work, we show that similar results can be obtained using the notion of IC-Envy. The advantage of IC-Envy is its efficiency: it can be computed using only the auction’s outcome. In particular, we focus on position auctions.