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
Implements the training, testing and editing tools for "Pluralistic Image Completion"

Pluralistic Image Completion ArXiv | Project Page | Online Demo | Video(demo) This repository implements the training, testing and editing tools for "

Chuanxia Zheng 615 Dec 08, 2022
CUAD

Contract Understanding Atticus Dataset This repository contains code for the Contract Understanding Atticus Dataset (CUAD), a dataset for legal contra

The Atticus Project 273 Dec 17, 2022
ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

ALFRED A Benchmark for Interpreting Grounded Instructions for Everyday Tasks Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han,

ALFRED 204 Dec 15, 2022
Active and Sample-Efficient Model Evaluation

Active Testing: Sample-Efficient Model Evaluation Hi, good to see you here! 👋 This is code for "Active Testing: Sample-Efficient Model Evaluation". P

Jannik Kossen 19 Oct 30, 2022
[ACM MM 2019 Oral] Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

Contents Cycle-In-Cycle GANs Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Acknowledgments Relat

Hao Tang 67 Dec 14, 2022
Official respository for "Modeling Defocus-Disparity in Dual-Pixel Sensors", ICCP 2020

Official respository for "Modeling Defocus-Disparity in Dual-Pixel Sensors", ICCP 2020 BibTeX @INPROCEEDINGS{punnappurath2020modeling, author={Abhi

Abhijith Punnappurath 22 Oct 01, 2022
Official code for "Decoupling Zero-Shot Semantic Segmentation"

Decoupling Zero-Shot Semantic Segmentation This is the official code for the arxiv. ZegFormer is the first framework that decouple the zero-shot seman

Jian Ding 108 Dec 30, 2022
190 Jan 03, 2023
验证码识别 深度学习 tensorflow 神经网络

captcha_tf2 验证码识别 深度学习 tensorflow 神经网络 使用卷积神经网络,对字符,数字类型验证码进行识别,tensorflow使用2.0以上 目前项目还在更新中,诸多bug,欢迎提出issue和PR, 希望和你一起共同完善项目。 实例demo 训练过程 优化器选择: Adam

5 Apr 28, 2022
ReferFormer - Official Implementation of ReferFormer

The official implementation of the paper: Language as Queries for Referring Video Object Segmentation Language as Queries for Referring Video Object S

Jonas Wu 232 Dec 29, 2022
Tutorial repo for an end-to-end Data Science project

End-to-end Data Science project This is the repo with the notebooks, code, and additional material used in the ITI's workshop. The goal of the session

Deena Gergis 127 Dec 30, 2022
Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

tonne 1.4k Dec 29, 2022
An Approach to Explore Logistic Regression Models

User-centered Regression An Approach to Explore Logistic Regression Models This tool applies the potential of Attribute-RadViz in identifying correlat

0 Nov 12, 2021
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations This is the repository for the paper Consumer Fairness in Recomm

7 Nov 30, 2022
Kaggle competition: Springleaf Marketing Response

PruebaEnel Prueba Kaggle-Springleaf-master Prueba Kaggle-Springleaf Kaggle competition: Springleaf Marketing Response Competencia de Kaggle: Marketing

1 Feb 09, 2022
MARS: Learning Modality-Agnostic Representation for Scalable Cross-media Retrieva

Introduction This is the source code of our TCSVT 2021 paper "MARS: Learning Modality-Agnostic Representation for Scalable Cross-media Retrieval". Ple

7 Aug 24, 2022
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
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis

A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis Figure: Shape-Accurate 3D-Aware Image Synthesis. A Shading-Guid

Xingang Pan 115 Dec 18, 2022
Source code for Zalo AI 2021 submission

zalo_ltr_2021 Source code for Zalo AI 2021 submission Solution: Pipeline We use the pipepline in the picture below: Our pipeline is combination of BM2

128 Dec 27, 2022
PyTorch GPU implementation of the ES-RNN model for time series forecasting

Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm A GPU-enabled version of the hybrid ES-RNN model by Slawek et al that won the M4 time-series

Kaung 305 Jan 03, 2023