MIRACLE (Missing data Imputation Refinement And Causal LEarning)

$van_der_Schaar \LAB$

Last update: Dec 29, 2022

Related tags

Overview

MIRACLE (Missing data Imputation Refinement And Causal LEarning)

Code Author: Trent Kyono

This repository contains the code used for the "MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms" paper(2021).

Installation

pip install -r requirements.txt
pip install .

Tests

You can run the tests using

pip install -r requirements_dev.txt
pip install .
pytest -vsx

miracle/MIRACLE.py - Imputer/Refiner Class. This class takes a baseline imputation and returns a refined imputation. This code has been forked from [2].
miracle/third_party - Reference imputers: Mean, Missforest, MICE, GAIN, Sinkhorn, KNN.
tests/run_example.py - runs a nonlinear toy DAG example. Uses mean imputation as a baseline and applies MIRACLE to refine.

Examples

Base example on toy dag.

$ cd tests/
$ python run_example.py

This specific instantiation returns a Baseline RMSE of approximately 0.95 with MIRACLE RMSE of approximately 0.40.

An example to run toy example with a dataset size of 2000 for 300 max_steps with a missingness of 30%

$ python3 run_example.py --dataset_sz 2000 --max_steps 300 --missingness 0.3

Citing

@inproceedings{kyono2021miracle,
	title        = {MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms},
	author       = {Kyono, Trent and Zhang, Yao and Bellot, Alexis and van der Schaar, Mihaela},
	year         = 2021,
	booktitle    = {Conference on Neural Information Processing Systems(NeurIPS) 2021}
}

References

[1] Jinsung Yoon, James Jordon, and Mihaela van der Schaar. Gain: Missing data imputation using generative adversarial nets. In ICML, 2018.

[2] Trent Kyono, Yao Zhang, and Mihaela van der Schaar. CASTLE: Regularization via auxiliary causal graph discovery. In NeurIPS, 2020.

[3] Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018). DAGs with NO TEARS: Continuous optimization for structure learning (NeurIPS 2018).

[4] Zheng, X., Dan, C., Aragam, B., Ravikumar, P., & Xing, E. P. (2020). Learning sparse nonparametric DAGs (AISTATS 2020).

Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)

Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021) Authors: Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song Link to pap

2 Dec 20, 2021

Code for NeurIPS 2021 paper: Invariant Causal Imitation Learning for Generalizable Policies

Invariant Causal Imitation Learning for Generalizable Policies Ioana Bica, Daniel Jarrett, Mihaela van der Schaar Neural Information Processing System

17 Dec 1, 2022

Official repository for "PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation"

pair-emnlp2020 Official repository for the paper: Xinyu Hua and Lu Wang: PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long

31 Oct 13, 2022

[CVPR 2021] Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach

Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach This is the repo to host the dataset TextSeg and code for TexRNe

174 Dec 19, 2022

Official implementation for (Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation, CVPR-2021)

FRSKD Official implementation for Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation (CVPR-2021) Requirements Pytho

75 Dec 28, 2022

Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Recently, the power of unconditional image synthesis has significantly advanced th

967 Jan 4, 2023

PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement.

DECOR-GAN PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement, Zhiqin Chen, Vladimir G. Kim, Matthew Fish

72 Dec 31, 2022

code for `Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation`

Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation (CVPR 2021) Introduction PBR is a conceptually simple yet effective

143 Jan 5, 2023

House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects

House-GAN++ Code and instructions for our paper: House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent

122 Dec 28, 2022

Releases(v0.1.5)

v0.1.5(Dec 27, 2022)
Changelog:

logging fixes

TF >= 2

Source code(tar.gz)
Source code(zip)
v0.1.4(Dec 12, 2022)

Update depends
Source code(tar.gz)
Source code(zip)
v0.1.3(May 25, 2022)
Random seed parameter

Source code(tar.gz)
Source code(zip)

MIRACLE (Missing data Imputation Refinement And Causal LEarning)

Related tags

Overview

MIRACLE (Missing data Imputation Refinement And Causal LEarning)

Installation

Tests

Contents

Examples

Citing

References

You might also like...

Multi-task Learning of Order-Consistent Causal Graphs (NeuRIPs 2021)

Code for NeurIPS 2021 paper: Invariant Causal Imitation Learning for Generalizable Policies

Official repository for "PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation"

[CVPR 2021] Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach

Official implementation for (Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation, CVPR-2021)

Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement.

code for `Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation`

House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects

Releases(v0.1.5)

v0.1.5(Dec 27, 2022)

v0.1.4(Dec 12, 2022)

v0.1.3(May 25, 2022)

Owner

van_der_Schaar \LAB

Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

Simulating Sycamore quantum circuits classically using tensor network algorithm.

Minimal implementation and experiments of "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging".

Official PyTorch implementation of "VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization" (CVPR 2021)

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

Bayesian Generative Adversarial Networks in Tensorflow

imbalanced-DL: Deep Imbalanced Learning in Python

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks

Code accompanying the paper "Wasserstein GAN"

Pytorch implementation for Patient Knowledge Distillation for BERT Model Compression

Code repository for the paper: Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild (ICCV 2021)

Running AlphaFold2 (from ColabFold) in Azure Machine Learning

graph-theoretic framework for robust pairwise data association

A community run, 5-day PyTorch Deep Learning Bootcamp

This repo is about to create the Streamlit application for given ML model.

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

Repository for the Bias Benchmark for QA dataset.

Simple and ready-to-use tutorials for TensorFlow

A Real-ESRGAN equipped Colab notebook for CLIP Guided Diffusion