Code for "Universal inference meets random projections: a scalable test for log-concavity"

Overview

How to use this repository

This repository contains code to replicate the results of "Universal inference meets random projections: a scalable test for log-concavity" by Robin Dunn, Larry Wasserman, and Aaditya Ramdas.

Folder contents

  • batch_scripts: Contains SLURM batch scripts to run the simulations. Scripts are labeled by the figure for which their simulations produce data. These scripts run the code in sim_code, using the parameters in sim_params.
  • data: Output of simulations.
  • plot_code: Reads simulation outputs from data and reproduces all figures in the paper. Plots are saved to plots folder.
  • plots: Contains all plots in paper.
  • sim_code: R code to run simulations. Simulation output is saved to data folder.
  • sim_params: Parameters for simulations. Each row contains a single choice of parameters. The scripts in sim_code read in these files, and the scripts in batch_scripts loop through all choices of parameters.

How do I ...

Produce the simulations for a given figure?

In the batch_scripts folder, scripts are labeled by the figure for which they simulate data. Run all batch scripts corresponding to the figure of interest. The allocated run time is estimated from the choice of parameters for which the code has the longest run time. Many scripts will run faster than this time. The files in sim_code each contain progress bars to estimate the remaining run time. You may wish to start running these files outside of a batch submission to understand the run time on your computing system.

Alternatively, to run the code without using a job submission system, click on any .sh file. The Rscript lines can be run on a terminal, replacing $SLURM_ARRAY_TASK_ID with all of the indices in the batch array.

The simulation output will be stored in the data folder, with one dataset per choice of parameters. To combine these datasets into a single dataset (as they currently appear in data), run the code in sim_code/combine_datasets.R.

Example: batch_scripts/fig01_fully_NP_randproj.sh

This script reproduces the universal test simulations for Figure 1. To do this, it runs the R script at sim_code/fig01_fully_NP_randproj.R. It reads in the parameters from sim_params/fig01_fully_NP_randproj_params.csv. There are 30 sets of parameters in total. The results will be stored in the data folder, with names such as fig01_fully_NP_randproj_1.csv, ..., fig01_fully_NP_randproj_30.csv. To combine these files into a single .csv file, run the code at sim_code/combine_datasets.R.

Examine the code for a given simulation?

The R code in sim_code is labeled by the figures for which they simulate data. Click on all files corresponding to a given figure.

Reproduce a figure without rerunning the simulations?

The R scripts in plot_code are labeled by their corresponding plots. They read in the necessary simulated data from the data folder and output the figures to the plots folder.

Owner
Robin Dunn
Principal Statistical Consultant, Novartis PhD in Statistics, Carnegie Mellon, 2021
Robin Dunn
Denoising Normalizing Flow

Denoising Normalizing Flow Christian Horvat and Jean-Pascal Pfister 2021 We combine Normalizing Flows (NFs) and Denoising Auto Encoder (DAE) by introd

CHrvt 17 Oct 15, 2022
Language Used: Python . Made in Jupyter(Anaconda) notebook.

FACE-DETECTION-ATTENDENCE-SYSTEM Made in Jupyter(Anaconda) notebook. Language Used: Python Steps to perform before running the program : Install Anaco

1 Jan 12, 2022
Project repo for the paper SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition

SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition (BMVC 2021) Project repo for the paper SILT: Self-supervised Lighting Trans

6 Dec 04, 2022
A Gura parser implementation for Python

Gura Python parser This repository contains the implementation of a Gura (compliant with version 1.0.0) format parser in Python. Installation pip inst

Gura Config Lang 19 Jan 25, 2022
Pytorch Implementation of Residual Vision Transformers(ResViT)

ResViT Official Pytorch Implementation of Residual Vision Transformers(ResViT) which is described in the following paper: Onat Dalmaz and Mahmut Yurt

ICON Lab 41 Dec 08, 2022
Official PyTorch implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

U-GAT-IT — Official PyTorch Implementation : Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Imag

Hyeonwoo Kang 2.4k Jan 04, 2023
Parametric Contrastive Learning (ICCV2021)

Parametric-Contrastive-Learning This repository contains the implementation code for ICCV2021 paper: Parametric Contrastive Learning (https://arxiv.or

DV Lab 156 Dec 21, 2022
Website for D2C paper

D2C This is the repository that contains source code for the D2C Website. If you find D2C useful for your work please cite: @article{sinha2021d2c au

1 Oct 21, 2021
CoaT: Co-Scale Conv-Attentional Image Transformers

CoaT: Co-Scale Conv-Attentional Image Transformers Introduction This repository contains the official code and pretrained models for CoaT: Co-Scale Co

mlpc-ucsd 191 Dec 03, 2022
MRI reconstruction (e.g., QSM) using deep learning methods

deepMRI: Deep learning methods for MRI Authors: Yang Gao, Hongfu Sun This repo is devloped based on Pytorch (1.8 or later) and matlab (R2019a or later

Hongfu Sun 17 Dec 18, 2022
A pytorch &keras implementation and demo of Fastformer.

Fastformer Notes from the authors Pytorch/Keras implementation of Fastformer. The keras version only includes the core fastformer attention part. The

153 Dec 28, 2022
This is the repository for Learning to Generate Piano Music With Sustain Pedals

SusPedal-Gen This is the official repository of Learning to Generate Piano Music With Sustain Pedals Demo Page Dataset The dataset used in this projec

Joann Ching 12 Sep 02, 2022
GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.

The GT4SD (Generative Toolkit for Scientific Discovery) is an open-source platform to accelerate hypothesis generation in the scientific discovery process. It provides a library for making state-of-t

Generative Toolkit 4 Scientific Discovery 142 Dec 24, 2022
Plug-n-Play Reinforcement Learning in Python with OpenAI Gym and JAX

coax is built on top of JAX, but it doesn't have an explicit dependence on the jax python package. The reason is that your version of jaxlib will depend on your CUDA version.

128 Dec 27, 2022
MVSDF - Learning Signed Distance Field for Multi-view Surface Reconstruction

MVSDF - Learning Signed Distance Field for Multi-view Surface Reconstruction This is the official implementation for the ICCV 2021 paper Learning Sign

110 Dec 20, 2022
DvD-TD3: Diversity via Determinants for TD3 version

DvD-TD3: Diversity via Determinants for TD3 version The implementation of paper Effective Diversity in Population Based Reinforcement Learning. Instal

3 Feb 11, 2022
Hooks for VCOCO

Verbs in COCO (V-COCO) Dataset This repository hosts the Verbs in COCO (V-COCO) dataset and associated code to evaluate models for the Visual Semantic

Saurabh Gupta 131 Nov 24, 2022
To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

Larissa Sayuri Futino Castro dos Santos 1 Jan 20, 2022
Download files from DSpace systems (because for some reason DSpace won't let you)

DSpaceDL A tool for downloading files from DSpace items. For some reason, DSpace systems have a dogshit UI, and Universities absolutely LOOOVE to use

Soumitra Shewale 5 Dec 01, 2022
This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

Intro This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales Vehicle Sam

39 Jul 21, 2022