Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning

no code implementations • 5 Jun 2023 • Aditya Srinivas Timmaraju, Mehdi Mashayekhi, Mingliang Chen, Qi Zeng, Quintin Fettes, Wesley Cheung, Yihan Xiao, Manojkumar Rangasamy Kannadasan, Pushkar Tripathi, Sean Gahagan, Miranda Bogen, Rob Roudani

While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta's ads systems.

Fairness Privacy Preserving +2

Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices

no code implementations • 17 Apr 2023 • Quintin Fettes, Avinash Karanth, Razvan Bunescu, Brandon Beckwith, Sreenivas Subramoney

Many cloud applications are migrated from the monolithic model to a microservices framework in which hundreds of loosely-coupled microservices run concurrently, with significant benefits in terms of scalability, rapid development, modularity, and isolation.

reinforcement-learning