Skip to content
View csinva's full-sized avatar
🎯
Focusing
🎯
Focusing
457 followers · 22 following

Achievements

Achievement: QuickdrawAchievement: Starstruckx3Achievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor
BetaSend feedback

Achievements

Achievement: QuickdrawAchievement: Starstruckx3Achievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor
BetaSend feedback

Organizations

@microsoft @conda-forge @BerkeleyML @Yu-Group @lumosvision @response4life @MicrosoftCopilot
Block or Report

Block or report csinva

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
csinva/README.md 

Hi there 👋 I'm Chandan, a Senior Researcher at Microsoft Research working on interpretable machine learning.
Homepage / Twitter / Google Scholar / LinkedIn

🌳 Interpretable models / dataset explanations

Interpretable and accurate predictive modeling, sklearn-compatible (JOSS 2021). Contains FIGS (arXiv 2022) and HSTree (ICML 2022)

Interpretability for text. Contains Aug-imodels (Nature Communications 2023) , iPrompt (ICLR workshop 2023) , SASC (arXiv 2023) , and Tree-Prompt (EMNLP 2023)

adaptive-wavelets Adaptive, interpretable wavelets (NeurIPS 2021)

🤖 General-purpose AI packages and cheatsheets

Notes and resources on AI

Utilities for trustworthy data-science (JOSS 2021)

🧠 Interpreting neural networks

deep-explanation-penalization Penalizing neural-network explanations (ICML 2020)

hierarchical-dnn-interpretations Hierarchical interpretations for neural network predictions (ICLR 2019)

transformation-importance Feature importance for transformations (ICLR Workshop 2020)

📊 Data-science problems

covid19-severity-prediction Extensive COVID-19 data + forecasting for counties and hospitals (HDSR 2021)

clinical-rule-vetting General pipeline for deriving clinical decision rules

iai-clinical-decision-rule Clinical decision rules for predicting intra-abdominal injury (PLOS Digital Health 2022)

molecular-partner-prediction Predicting successful CME events using only clathrin markers

Various aspects of deep learning and machine learning

gan-vae-pretrained-pytorch Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch

gpt2-paper-title-generator Generating paper titles with GPT-2

disentangled-attribution-curves Attribution curves for interpreting tree ensembles trees (arxiv 2019)

matching-with-gans Matching in GAN latent space for better bias benchmarking. (CVPR workshop 2021)

data-viz-utils Functions for easily making publication-quality figures with matplotlib

mdl-complexity Revisiting complexity and the bias-variance tradeoff (TOPML workshop 2021)

Projects advised

pasta Post-hoc Attention Steering for LLMs (ICLR 2024), led by Qingru Zhang

meta-tree Learning a Decision Tree Algorithm with Transformers (arXiv 2024), led by Yufan Zhuang

explanation-consistency-finetuning Consistent Natural-Language Explanations (arXiv 2024), led by Yanda Chen

Open-source contributions

Major: autogluon , big-bench , nl-augmenter

Minor: conference-acceptance-rates , iterative-random-forest , interpretable-ml-book , awesome-interpretable-machine-learning , awesome-machine-learning-interpretability , awesome-llm-interpretability , executable-books , deep-fMRI-dataset

Mini-projects

hummingbird-tracking, imodels-experiments, cookiecutter-ml-research, nano-descriptions, news-title-bias, java-mini-games, imodels-data, news-balancer, arxiv-copier, dnn-experiments, max-activation-interpretation-pytorch, acronym-generator, hpa-interp, sensible-local-interpretations, global-sports-analysis, mouse-brain-decoding, ...

Pinned

  1. csinva.github.io csinva.github.io Public

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

    HTML 535 104

  2. imodels imodels Public

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

    Jupyter Notebook 1.3k 113

  3. hierarchical-dnn-interpretations hierarchical-dnn-interpretations Public

    Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

    Jupyter Notebook 124 22

  4. laura-rieger/deep-explanation-penalization laura-rieger/deep-explanation-penalization Public

    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

    Jupyter Notebook 123 13

  5. Yu-Group/covid19-severity-prediction Yu-Group/covid19-severity-prediction Public

    Extensive and accessible COVID-19 data + forecasting for counties and hospitals. 📈

    Jupyter Notebook 227 92

  6. gan-vae-pretrained-pytorch gan-vae-pretrained-pytorch Public

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

    Jupyter Notebook 170 42