Slides, paper notes, class notes, blog posts, and research on ML 📉, statistics 📊, and AI 🤖.

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

Interpret text data with LLMs (sklearn compatible).

readme

A logical, reasonably standardized, but flexible project structure for conducting ml research 🍪

Extension for copying the title + url of an arXiv page via right click

Building and vetting clinical decision rules.

Analyzing clinical decision instruments through the lens of data and large language models.

Analyzing interview data from the PediDOSE EFIC interviews using LLMs.

Experiments with language fMRI data from Alex Huth lab. More organized repo here: https://github.com/microsoft/automated-brain-explanations

Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.

Interpretable text embeddings by asking LLMs yes/no questions (NeurIPS 2024)

Scraping and analyzing political bias in news titles using data from allsides.com

Functions for easily making publication-quality figures with matplotlib.

Create a tree of prompts during training that improves efficiency and accuracy.

Training LLMs for matrix completion

Preprocessed data for various popular tabular datasets to go along with imodels.

Finding semantically meaningful and accurate prompts.

Explaining data to humans with linear models + LLM hints.

Tree prompting: easy-to-use scikit-learn interface for improved prompting.