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

A Fast and Accurate One-Stage Approach to Visual Grounding, ICCV 2019 (Oral)

FlowTorch is a PyTorch library for learning and sampling from complex probability distributions using a class of methods called Normalizing Flows

Shuffle Attention for MobileNetV3

Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data

A general 3D Object Detection codebase in PyTorch.

Angle data is a simple data type.

Official implementation of the paper 'Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution' in CVPR 2022

A collection of pre-trained StyleGAN2 models trained on different datasets at different resolution.

Official PyTorch implementation of "Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient".

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)

ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton (AAAI 2022)

A basic duplicate image detection service using perceptual image hash functions and nearest neighbor search, implemented using faiss, fastapi, and imagehash

A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).

Automatically erase objects in the video, such as logo, text, etc.

Unofficial PyTorch implementation of MobileViT based on paper "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

Code for "Continuous-Time Meta-Learning with Forward Mode Differentiation" (ICLR 2022)

Tutorial for the PERFECTING FACTORY 5.0 WITH EDGE-POWERED AI workshop

source code and pre-trained/fine-tuned checkpoint for NAACL 2021 paper LightningDOT

Earth Vision Foundation

Joint parameterization and fitting of stroke clusters