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

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

Experiments with language fMRI data from Alex Huth lab

readme

Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.

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

Interpretable text embeddings by asking LLMs yes/no questions

Analyzing interview data from the PediDOSE EFIC interviews using LLMs.

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.

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

Interpretable clinical decision rules for predicting intra-abdominal injury.

Generating paper titles (and more!) with GPT trained on data scraped from arXiv.

MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".

Fork of pybaobabdt adding more customization.

Matching in GAN latent space for better bias benchmarking and semantic image editing. 👶🏻🧒🏾👩🏼‍🦰👱🏽‍♂️👴🏾

Generator acronyms given a sequence of words (useful for making paper titles).

Demos for visualizing how rule-based models work.