Code related to the manuscript "Averting A Crisis In Simulation-Based Inference"

Overview

Abstract

We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms are inadequate for the falsificationist methodology of scientific inquiry. Our results collected through massive experimental computations show that all benchmarked algorithms -- (S)NPE, (S)NRE, SNL and variants of ABC -- may produce overconfident posterior approximations, which makes them demonstrably unreliable and dangerous if one's scientific goal is to constrain parameters of interest. We believe that failing to address this issue will lead to a well-founded trust crisis in simulation-based inference. For this reason, we argue that research efforts should now focus on theoretical and methodological developments of conservative approximate inference algorithms and present research directions towards this objective. In this regard, we show empirical evidence that ensembles are consistently more reliable.

A PDF render of the manuscript is available in this repo or on ArXiV.

Using the code

Recommended. This installs a Python 3 environment by default.

[email protected]:~ $ wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
[email protected]:~ $ sh Miniconda3-latest-Linux-x86_64.sh

Next, install the necessary dependencies.

[email protected]:~ conda env create -f environment.yml
[email protected]:~ conda activate crisissbi

After the environment has been activated, there are 2 ways to execute the pipelines depending on your setup. The first only requires your laptop. In that regard simply execute a pipeline as follows:

[email protected]:~ cd workflows/auc_demonstration
[email protected]:~ python pipeline.py

The other approach is on a Slurm enabled HPC cluster. To exploit the parallelism, execute the script as

[email protected]:~ cd workflows/auc_demonstration
[email protected]:~ python pipeline.py --slurm

The jobs will be automatically submitted to the default Slurm queue.

Citation

See CITATION.cff

License

Described the LICENSE file.

Owner
Montefiore Artificial Intelligence Research
Artificial Intelligence Research at the Montefiore Institute of the University of Liège.
Montefiore Artificial Intelligence Research
C3D is a modified version of BVLC caffe to support 3D ConvNets.

C3D C3D is a modified version of BVLC caffe to support 3D convolution and pooling. The main supporting features include: Training or fine-tuning 3D Co

Meta Archive 1.1k Nov 14, 2022
Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation.

Distant Supervision for Scene Graph Generation Data and code for ICCV 2021 paper Distant Supervision for Scene Graph Generation. Introduction The pape

THUNLP 23 Dec 31, 2022
Linear algebra python - Number of operations and problems in Linear Algebra and Numerical Linear Algebra

Linear algebra in python Number of operations and problems in Linear Algebra and

Alireza 5 Oct 09, 2022
The implementation of 'Image synthesis via semantic composition'.

Image synthesis via semantic synthesis [Project Page] by Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia. Introduction This repository gives

DV Lab 71 Jan 06, 2023
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling For Official repo of NU-Wave: A Diffusion Probabilistic Model for Neural Audio Up

Rishikesh (ऋषिकेश) 38 Oct 11, 2022
Semi-supervised semantic segmentation needs strong, varied perturbations

Semi-supervised semantic segmentation using CutMix and Colour Augmentation Implementations of our papers: Semi-supervised semantic segmentation needs

146 Dec 20, 2022
Official repository of DeMFI (arXiv.)

DeMFI This is the official repository of DeMFI (Deep Joint Deblurring and Multi-Frame Interpolation). [ArXiv_ver.] Coming Soon. Reference Jihyong Oh a

Jihyong Oh 56 Dec 14, 2022
Trainable PyTorch reproduction of AlphaFold 2

OpenFold A faithful PyTorch reproduction of DeepMind's AlphaFold 2. Features OpenFold carefully reproduces (almost) all of the features of the origina

AQ Laboratory 1.7k Dec 29, 2022
FairyTailor: Multimodal Generative Framework for Storytelling

FairyTailor: Multimodal Generative Framework for Storytelling

Eden Bens 172 Dec 30, 2022
Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets

Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets (including obl

Azavea 1.7k Dec 22, 2022
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

15 Nov 18, 2022
IMBENS: class-imbalanced ensemble learning in Python.

IMBENS: class-imbalanced ensemble learning in Python. Links: [Documentation] [Gallery] [PyPI] [Changelog] [Source] [Download] [知乎/Zhihu] [中文README] [a

Zhining Liu 176 Jan 04, 2023
Learning Continuous Signed Distance Functions for Shape Representation

DeepSDF This is an implementation of the CVPR '19 paper "DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation" by Park et a

Meta Research 1.1k Jan 01, 2023
这个开源项目主要是对经典的时间序列预测算法论文进行复现,模型主要参考自GluonTS,框架主要参考自Informer

Time Series Research with Torch 这个开源项目主要是对经典的时间序列预测算法论文进行复现,模型主要参考自GluonTS,框架主要参考自Informer。 建立原因 相较于mxnet和TF,Torch框架中的神经网络层需要提前指定输入维度: # 建立线性层 TensorF

Chi Zhang 85 Dec 29, 2022
[CIKM 2021] Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. This repo contains the PyTorch code and implementation for the paper E

Akuchi 18 Dec 22, 2022
ZeroGen: Efficient Zero-shot Learning via Dataset Generation

ZEROGEN This repository contains the code for our paper “ZeroGen: Efficient Zero

Jiacheng Ye 31 Dec 30, 2022
Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed. KDD 2019.

gHHC Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, D

Nicholas Monath 35 Nov 16, 2022
An Unsupervised Detection Framework for Chinese Jargons in the Darknet

An Unsupervised Detection Framework for Chinese Jargons in the Darknet This repo is the Python 3 implementation of 《An Unsupervised Detection Framewor

7 Nov 08, 2022
Ascend your Jupyter Notebook usage

Jupyter Ascending Sync Jupyter Notebooks from any editor About Jupyter Ascending lets you edit Jupyter notebooks from your favorite editor, then insta

Untitled AI 254 Jan 08, 2023
🗺 General purpose U-Network implemented in Keras for image segmentation

TF-Unet General purpose U-Network implemented in Keras for image segmentation Getting started • Training • Evaluation Getting started Looking for Jupy

Or Fleisher 2 Aug 31, 2022