Excerpts and takeaways from what our team is reading to inspire and inform what we build
This week we reviewed techniques using simple Bayesian algorithms for sequential decision-making problems.
This week, we discuss glment, the widely-renowned, frequently-used, and efficient Lasso optimization package.
In this conversation, we take a look at Prophet, an automatic, open-source, time-series forecasting tool by Facebook.
A discussion on R-learner, a 2-step causal inference algorithm to estimate heterogeneous treatment effects from observational data.
A look at a novel method for False Discovery Rate control in variable selection — the Fixed-X Knockoff filter by Rina Barber and Emmanuel Candès.
A discussion of Quasi-Newton methods that create approximations that meet this criterion and thus enjoy global and local convergence properties.
The first part in a series of papers we're reading on Quasi-Newton methods, which Sisu relies on for optimizing key subproblems when running workflows.
A discussion on Benjamini-Hochberg, avoiding the multi-comparison problem, and False Discovery Rate (FDR).
This week, we take a look at ScaNN (Scalable Nearest Neighbors), a method for efficient vector similarity search at scale.
In this weeks discussion, we review the Synthetic Controls method, which extends potential outcomes form Causal Inference literature to time-dependent observational data.