Hi there ๐ I'm Chandan, a Senior Researcher at Microsoft Research working on interpretable machine learning. Homepage / Twitter / LinkedIn / Google Scholar
๐ค General-purpose AI packages and cheatsheets
Slides, paper notes, class notes, blog posts, and research on ML, stat, and AI
Making it easier to build stable, trustworthy data-science pipelines (JOSS 2021)
๐ณ Interpretable models / dataset explanations
Interpretable and accurate predictive modeling, sklearn-compatible (JOSS 2021) -- contains FIGS (arXiv 2022) and HSTree (ICML 2022)
Library to explain a dataset in natural language -- contains Aug-imodels (arXiv 2023) , iPrompt (arXiv 2023) , and SASC (arXiv 2023)
adaptive-wavelets Adaptive, interpretable wavelets across domains (NeurIPS 2021)
๐ง Interpreting neural networks
deep-explanation-penalization Interpretations are useful: penalizing explanations to align neural networks with prior knowledge (ICML 2020)
hierarchical-dnn-interpretations Hierarchical interpretations for neural network predictions (ICLR 2019)
transformation-importance Transformation Importance with Applications to Cosmology (ICLR Workshop 2020)
๐ Data-science problems
covid19-severity-prediction Extensive and accessible COVID-19 data + forecasting for counties and hospitals (HDSR 2021)
clinical-rule-vetting General pipeline for deriving clinical decision rules
iai-clinical-decision-rule Interpretable 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 Disentangled Attribution Curves for Interpreting Random Forests and Boosted 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)
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 , 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, ...