Learning, Unsupervised

Excerpts and takeaways from what our team is reading to inspire and inform what we build

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Causal inference


Statistical inference

ML systems

Simple Bayesian Algorithms for Best Arm Identification | March 9, 2021

This week we reviewed techniques using simple Bayesian algorithms for sequential decision-making problems.

glmnet | February 23, 2021

This week, we discuss glment, the widely-renowned, frequently-used, and efficient Lasso optimization package.

Prophet | February 9, 2021

In this conversation, we take a look at Prophet, an automatic, open-source, time-series forecasting tool by Facebook.

R-Learner | December 7, 2020

A discussion on R-learner, a 2-step causal inference algorithm to estimate heterogeneous treatment effects from observational data.

Fixed-X knockoffs | November 9, 2020

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.

Quasi-Newton part two: BFGS | October 27, 2020

A discussion of Quasi-Newton methods that create approximations that meet this criterion and thus enjoy global and local convergence properties.

Quasi-Newton part one: Wolfe conditions | October 13, 2020

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.

Benjamini-Hochberg | August 17, 2020

A discussion on Benjamini-Hochberg, avoiding the multi-comparison problem, and False Discovery Rate (FDR).

SCANN | August 3, 2020

This week, we take a look at ScaNN (Scalable Nearest Neighbors), a method for efficient vector similarity search at scale.

The Synthetic Controls Method | July 6, 2020

In this weeks discussion, we review the Synthetic Controls method, which extends potential outcomes form Causal Inference literature to time-dependent observational data.

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