csinva
Senior researcher @Microsoft interpreting ML models in science and medicine. PhD from UC Berkeley.
Senior researcherMicrosoft research
Pinned Repositories
csinva.github.io
Slides, paper notes, class notes, blog posts, and research on ML π, statistics π, and AI π€.
gan-vae-pretrained-pytorch
Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
gpt-paper-title-generator
Generating paper titles (and more!) with GPT trained on data scraped from arXiv.
hierarchical-dnn-interpretations
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" π§ (ICLR 2019)
imodels
Interpretable ML package π for concise, transparent, and accurate predictive modeling (sklearn-compatible).
imodelsX
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
iprompt
Finding semantically meaningful and accurate prompts.
tree-prompt
Tree prompting: easy-to-use scikit-learn interface for improved prompting.
deep-explanation-penalization
Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584
covid19-severity-prediction
Extensive and accessible COVID-19 data + forecasting for counties and hospitals. π
csinva's Repositories
csinva/imodels
Interpretable ML package π for concise, transparent, and accurate predictive modeling (sklearn-compatible).
csinva/csinva.github.io
Slides, paper notes, class notes, blog posts, and research on ML π, statistics π, and AI π€.
csinva/gpt-paper-title-generator
Generating paper titles (and more!) with GPT trained on data scraped from arXiv.
csinva/imodelsX
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
csinva/iprompt
Finding semantically meaningful and accurate prompts.
csinva/tree-prompt
Tree prompting: easy-to-use scikit-learn interface for improved prompting.
csinva/matching-with-gans
Matching in GAN latent space for better bias benchmarking and semantic image editing. πΆπ»π§πΎπ©πΌβπ¦°π±π½ββοΈπ΄πΎ
csinva/data-viz-utils
Functions for easily making publication-quality figures with matplotlib.
csinva/mdl-complexity
MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".
csinva/cookiecutter-ml-research
A logical, reasonably standardized, but flexible project structure for conducting ml research πͺ
csinva/iai-clinical-decision-rule
Interpretable clinical decision rules for predicting intra-abdominal injury.
csinva/interpretable-embeddings
csinva/clinical-rule-analysis
Analyzing clinical decision instruments through the lens of data and large language models.
csinva/tree-prompt-experiments
Create a tree of prompts during training that improves efficiency and accuracy.
csinva/acronym-generator
Generator acronyms given a sequence of words (useful for making paper titles).
csinva/imodels-data
Preprocessed data for various popular tabular datasets to go along with imodels.
csinva/tpr-fmri
csinva/imodels-playground
Demos for visualizing how rule-based models work.
csinva/inverse-scaling
A prize for finding tasks that cause large language models to show inverse scaling
csinva/pybaobab-fork
Fork of pybaobabdt adding more customization.
csinva/csinva
readme
csinva/fmri
csinva/matrix-completion-llm
Training LLMs for matrix completion
csinva/news-title-bias
Scraping and analyzing political bias in news titles using data from allsides.com
csinva/pedidose-efic-analysis
Analyzing interview data from the PediDOSE EFIC interviews using LLMs.
csinva/pyGAM
[HELP REQUESTED] Generalized Additive Models in Python
csinva/awesome-llm-interpretability
A curated list of Large Language Model (LLM) Interpretability resources.
csinva/gam-experiments
csinva/llm-guided-data-explanation
Explaining data to humans with linear models + LLM hints.
csinva/r2d3-decision-tree
A clone of the animated decision tree at http://www.r2d3.us/ in React, React-Motion, and d3