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High-dimensional Bayesian optimization with sparsity-inducing priors
This work was a collaboration with Martin Jankowiak (Broad Institute of Harvard and MIT). What the research is: Sparse axis-aligned…
News
Optimizing model accuracy and latency using Bayesian multi-objective neural architecture search
What the research is: We propose a method for sample-efficient optimization of the trade-offs between model accuracy and on-device prediction…
News
Q&A with Christian Kroer, Columbia professor and Facebook Fellowship alum
In this monthly interview series, we turn the spotlight on members of the academic community and the important research they…
News
Core Data Science researchers discuss joining Facebook without backgrounds in computer science
The Core Data Science (CDS) team at Facebook works with a variety of teams on an ever-changing range of projects…
News
Announcing the winners of the Sample-Efficient Sequential Bayesian Decision Making request for proposals
In February 2021, Facebook launched a request for proposals (RFP) on sample-efficient sequential Bayesian decision-making. Today, we’re announcing the winners…
RFP
2021 Statistics for Improving Insights, Models, and Decisions request for proposals
At Facebook, our research teams strive to improve decision making for a business that touches the lives of billions of…
News
Cost-sensitive exploration in multi-armed bandits: Application to SMS routing
What the research is: Many businesses, including Facebook, send SMS messages to their users for phone number verification, two-factor authentication,…
Publication
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.
Publication
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.
News
Auto-placement of ad campaigns using multi-armed bandits
What the research is: We look at the problem of allocating the budget of an advertiser across multiple surfaces optimally…
