jmdvinodjmd's Stars
google-research/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
afshinea/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
ujjwalkarn/Machine-Learning-Tutorials
machine learning and deep learning tutorials, articles and other resources
bharathgs/Awesome-pytorch-list
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
rguo12/awesome-causality-algorithms
An index of algorithms for learning causality with data
matheusfacure/python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
GeostatsGuy/PythonNumericalDemos
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
jvpoulos/causal-ml
Must-read papers and resources related to causal inference and machine (deep) learning
ott-jax/ott
Optimal transport tools implemented with the JAX framework, to get differentiable, parallel and jit-able computations.
rguo12/awesome-causality-data
A data index for learning causality.
msuzen/looper
A resource list for causality in statistics, data science and physics
AvivSham/pFedHN
Official code implementation for "Personalized Federated Learning using Hypernetworks" [ICML 2021]
chrhenning/hypnettorch
Package for working with hypernetworks in PyTorch.
GregVial/CoMNIST
Cyrillic-oriented MNIST. A dataset of Latin and Cyrillic letter images for text recognition.
lstruski/Processing-of-missing-data-by-neural-networks
divyat09/cate-estimator-selection
Code accompanying the paper "Empirical analysis of model selection for heterogeneous causal effect estimation"
ZhaozhiQIAN/Single-Cause-Perturbation-NeurIPS-2021
Code for Estimating Multi-cause Treatment Effects via Single-cause Perturbation (NeurIPS 2021)
misoc-mml/hyperparam-sensitivity
Investigating sensitivity of CATE estimators to the choice of hyperparameters.
jmdvinodjmd/HyperITE
HyperITE
ku-milab/MIAM
Pytorch implementation of "Multi-view Integration Learning for Irregularly-sampled Clinical Time Series" (Under review, JBHI)
Ghadeer-Ghosheh/physionet2012-timeseries
PhysioNet 2012 Challenge Time-series preprocessing pipeline
jadonzhou/PatientDischargePrediction
ifm-mag/DOTES-Forging-Consolidation-2023
Exploitation of material consolidation trade-offs in multi-tier complex supply networks
jmdvinodjmd/decision_curve
Decision curve analysis evaluates a predictor for an event as a probability threshold is varied, typically by showing a graphical plot of net benefit against threshold probability. By convention, the default strategies of assuming that all or no observations are positive are also plotted.
jmdvinodjmd/SSB_MLHC
mlresearch/v238
Proceedings of AISTATS 2024
tuur/CalibrationSlopeInterceptPython
Calculation of calibration slope, calibration intercept, and calibration in the large for binary prediction models in Python.