The future of time-series forecasting, with RFP winner B. Aditya Prakash
From time to time, Meta invites academics to propose research in specific areas that align with our mission of building community and bringing the world closer together.
From time to time, Meta invites academics to propose research in specific areas that align with our mission of building community and bringing the world closer together.
The cumulative approach is currently unconventional, yet offers many favorable statistical properties, guaranteed via mathematical theory backed by rigorous proofs and...
We describe descriptive statistics related to how long engineers are able to focus. We find that at Meta, Engineers have a total of 14.25 hours of personal-focus time per week.
In this paper, we present our work on a code quality prediction framework, we call Automated Incremental Effort Investments (AIEI), to fasten the process of going from data...
In this paper, we present a real-world case study of an architectural refactoring project within an industrial setting. The system in scope is our codenamed ItemIndexer delivery...
What it’s like to be a parent on the Core Data Science team at Meta
We propose a feature-based approach based on a self-excited Hawkes point process model, which involves prediction of the content’s popularity at one or more reference horizons in tandem with a point predictor of an effective growth parameter that reflects the timescale of popularity growth.
Modeling contextual uncertainty in crowdsourcing using Gaussian Processes