## An Ensemble of Kan Extensions

Posted on August 14, 2022

A common problem in machine learning is “use this function defined over this small set to generate predictions over that larger set.” Extrapolation, interpolation, statistical inference and forecasting all reduce to this problem. The Kan extension is a powerful tool in category theory that generalizes this notion. In a recent...

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Tags:
Machine Learning, Category Theory

## Resilient Machine Learning

Posted on July 4, 2022

A boxer who only punches a bag will fail in the ring, and an ML model that only learns with clean data will fail in production. We need to let our model get punched in the face in training if we want it to perform well when distributions drift. The...

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Tags:
Machine Learning, Resilient, Mistakes

## Large Mistakes in Regression Problems

Posted on February 16, 2022

Many real world problems can be framed as regression: use a collection of features \(X\) to predict a real-valued quantity \(y\). However, this framing can obfuscate a very important detail: which kinds of mistakes are most important to avoid? Many factors can influence this, including how the predictions will be...

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Tags:
Machine Learning, Regression, ML

## Predicting a Quantity with Categorical Features

Posted on December 21, 2021

Introduction Many prediction problems can be framed as “given the knowledge that this sample belongs to categories \(A,B,C,\cdots,D\), predict something about this sample.” As a concrete example, suppose we would like to use linear regression to predict the value of a transaction based on a small set of categorical features...

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Tags:
Machine Learning, Machine Learning Systems, ML, Features

## Improving a Machine Learning System (Part 3 - A/B testing)

Posted on November 12, 2021

This post is part three in a three part series on the challenges of improving a production machine learning system. Find part one here and part two here. A/B Testing When engineers and data scientists optimize machine learning systems they often focus on improving offline metrics like cross entropy, ROC-AUC,...

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Tags:
Machine Learning, Machine Learning Systems, A/B, A/B testing