ML/Statistics

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

Nearly four years ago, I released autosum. Autosum exploits work by other scientists to harvest key points from (and key concerns with) a paper. The software grabs the sentence before or after the citation to build that knowledge. The output is pretty useful. See for yourself. But you could do

If the canonical insight of computer science is automating repetition, the canonical insight of data science is optimization. It isn’t that computer scientists haven’t thought about optimization. They have. But computer scientists weren’t the first to think about automation, just like economists weren’t the first to

Probabilities from classification models can have two problems: 1. Miscalibration: A p of .9 often doesn’t mean a 90% chance of 1 (assuming a dichotomous y). (You can calibrate it using isotonic regression.) 2. Optimal cut-offs: For multi-class classifiers, we do not know what probability value will maximize the

Say that you train a model to predict who will click on an ad. Say that you deploy the model to only show ads to people who are likely to click on them. (For a discussion about the optimal strategy for whom to show ads to, see here.) And say

Let’s say that we want to measure the effect of a phone call encouraging people to register to vote on voting. Let’s define compliance as a person taking the call (like they do in Gerber and Green, 2000, etc.). And let’s assume that the compliance rate is