ML/Statistics

Forward Stepwise Regression (FSR) is hardly used today. That is mostly because regularization is a better way to think about variable selection. But part of the reason for its disuse is that FSR is a greedy optimization strategy with unstable paths. Jigger the data a little, and the search paths,

Say that you want to persuade a group of people to go out and vote. You can reach people by phone, mail, f2f, or email. And the cost of reaching out f2f > phone > mail > email. Your objective is to convert as many people as possible. How would

This is not a note about George Box’s quote about models. Neither is it about explainability. The first is trite. And the second is a mug’s game. Imagine the following: you get hundreds of emails a day, and someone must manually sort which emails are urgent and which

Marketers love engagement ladders. To increase engagement with a product, many companies segment their users based on usage, for instance, into heavy (super), medium (average), and light, and prod their users to climb the ladder by suggesting they do things that people in the segment above them are doing and

I recently gave an interview to InfoQ about my paper (and associated open source software) on predicting the race and ethnicity of a person using the sequence of characters in a name. Here a relevant excerpt: InfoQ: Can you discuss how we can learn from names? What ML/DL algorithms

Trends in time series are valuable. If the cost of a product rises suddenly, it likely indicates a sudden shortfall in supply or a sudden rise in demand. If the cost of claims filed by a patient rises sharply, it plausibly suggests rapidly worsening health. But how do we estimate

Given a pool of messages, how can you maximize CTR? The problem of maximizing CTR reduces to the problem of estimating the probability that a person in a specific context will click on each of the messages. Once you have the probabilities, all you need to do is apply the

Say that you are building a news recommender that lists which relevant news items are in each person’s news feed. Say that your first version of the news recommender is a rules-based system that uses signals like how many people in your network have seen the news, how

For the uninitiated: A siamese neural network consists of twin networks which accept distinct inputs but are joined by an energy function at the top. This function computes some metric between the highest level feature representation on each side. The parameters between the twin networks are tied. Weight tying guarantees