Code accompanying the paper "How Tight Can PAC-Bayes be in the Small Data Regime?"

Related tags

Deep Learningpac-bayes-tightness-small-data
Overview

How Tight Can PAC-Bayes be in the Small Data Regime?

This is the code to reproduce all experiments for the following paper:

@inproceedings{Foong:2021:How_Tight_Can_PAC-Bayes_Be,
    title = {How Tight Can {PAC}-{Bayes} Be in the Small Data Regime?},
    year = {2021},
    author = {Andrew Y. K. Foong and Wessel P. Bruinsma and David R. Burt and Richard E. Turner},
    booktitle = {Advances in Neural Information Processing Systems},
    volume = {35},
    eprint = {https://arxiv.org/abs/2106.03542},
}

Every experiment creates a folder in _experiments. The names of the files in those folders should be self-explanatory.

Installation

First, create and activate a virtual environment for Python 3.8.

virtualenv venv -p python3.8 
source venv/bin/activate

Then install an appropriate GPU-accelerated version of PyTorch.

Finally, install the requirements for the project.

pip install -e .

You should now be able to run the below commands.

Generating Datasets

In order to generate the synthetic 1D datasets used, run these commands from inside classification_1d:

python gen_data.py --class_scheme balanced --num_context 30 --name 30-context --num_train_batches 5000 --num_test_batches 64
python gen_data.py --class_scheme balanced --num_context 60 --name 60-context --num_train_batches 5000 --num_test_batches 64

The generated datasets will be in pacbayes/_data_caches

Theory Experiments

See Figure 2 in Section 3 and Appendix G.

python theory_experiments.py --setting det1-1
python theory_experiments.py --setting det1-2
python theory_experiments.py --setting det2-1
python theory_experiments.py --setting det2-1

python theory_experiments.py --setting stoch1
python theory_experiments.py --setting stoch2
python theory_experiments.py --setting stoch3

python theory_experiments.py --setting random --random-seed 1 --random-better-bound maurer
python theory_experiments.py --setting random --random-seed 6 --random-better-bound catoni

GNP Classification Experiments

See Figure 3 and 4 in Section 4 and Appendices I and J. The numbers from the graphs can be found in eval_metrics_no_post_opt.txt (without post optimisation) eval_metrics_post_opt.txt (with post optimisation).

MODEL_NONDDP=maurer MODEL_DDP=maurer-ddp NUM_CONTEXT=30 ./run_GNP_prop_024.sh
MODEL_NONDDP=maurer MODEL_DDP=maurer-ddp NUM_CONTEXT=30 ./run_GNP_prop_68.sh
MODEL_NONDDP=catoni MODEL_DDP=catoni-ddp NUM_CONTEXT=30 ./run_GNP_prop_024.sh
MODEL_NONDDP=catoni MODEL_DDP=catoni-ddp NUM_CONTEXT=30 ./run_GNP_prop_68.sh
MODEL_NONDDP=convex-nonseparable MODEL_DDP=convex-nonseparable-ddp NUM_CONTEXT=30 ./run_GNP_prop_024.sh
MODEL_NONDDP=convex-nonseparable MODEL_DDP=convex-nonseparable-ddp NUM_CONTEXT=30 ./run_GNP_prop_68.sh
MODEL_NONDDP=kl-val MODEL_DDP=kl-val NUM_CONTEXT=30 ./run_GNP_prop_024.sh
MODEL_NONDDP=kl-val MODEL_DDP=kl-val NUM_CONTEXT=30 ./run_GNP_prop_68.sh
MODEL_NONDDP=maurer-optimistic MODEL_DDP=maurer-optimistic-ddp NUM_CONTEXT=30 ./run_GNP_prop_024.sh
MODEL_NONDDP=maurer-optimistic MODEL_DDP=maurer-optimistic-ddp NUM_CONTEXT=30 ./run_GNP_prop_68.sh
MODEL_NONDDP=maurer-inv MODEL_DDP=maurer-inv-ddp NUM_CONTEXT=30 ./run_GNP_prop_024.sh
MODEL_NONDDP=maurer-inv MODEL_DDP=maurer-inv-ddp NUM_CONTEXT=30 ./run_GNP_prop_68.sh
MODEL_NONDDP=maurer-inv-optimistic MODEL_DDP=maurer-inv-optimistic-ddp NUM_CONTEXT=30 ./run_GNP_prop_024.sh
MODEL_NONDDP=maurer-inv-optimistic MODEL_DDP=maurer-inv-optimistic-ddp NUM_CONTEXT=30 ./run_GNP_prop_68.sh

MODEL_NONDDP=maurer MODEL_DDP=maurer-ddp NUM_CONTEXT=60 ./run_GNP_prop_024.sh
MODEL_NONDDP=maurer MODEL_DDP=maurer-ddp NUM_CONTEXT=60 ./run_GNP_prop_68.sh
MODEL_NONDDP=catoni MODEL_DDP=catoni-ddp NUM_CONTEXT=60 ./run_GNP_prop_024.sh
MODEL_NONDDP=catoni MODEL_DDP=catoni-ddp NUM_CONTEXT=60 ./run_GNP_prop_68.sh
MODEL_NONDDP=convex-nonseparable MODEL_DDP=convex-nonseparable-ddp NUM_CONTEXT=60 ./run_GNP_prop_024.sh
MODEL_NONDDP=convex-nonseparable MODEL_DDP=convex-nonseparable-ddp NUM_CONTEXT=60 ./run_GNP_prop_68.sh
MODEL_NONDDP=kl-val MODEL_DDP=kl-val NUM_CONTEXT=60 ./run_GNP_prop_024.sh
MODEL_NONDDP=kl-val MODEL_DDP=kl-val NUM_CONTEXT=60 ./run_GNP_prop_68.sh
MODEL_NONDDP=maurer-optimistic MODEL_DDP=maurer-optimistic-ddp NUM_CONTEXT=60 ./run_GNP_prop_024.sh
MODEL_NONDDP=maurer-optimistic MODEL_DDP=maurer-optimistic-ddp NUM_CONTEXT=60 ./run_GNP_prop_68.sh
MODEL_NONDDP=maurer-inv MODEL_DDP=maurer-inv-ddp NUM_CONTEXT=60 ./run_GNP_prop_024.sh
MODEL_NONDDP=maurer-inv MODEL_DDP=maurer-inv-ddp NUM_CONTEXT=60 ./run_GNP_prop_68.sh
MODEL_NONDDP=maurer-inv-optimistic MODEL_DDP=maurer-inv-optimistic-ddp NUM_CONTEXT=60 ./run_GNP_prop_024.sh
MODEL_NONDDP=maurer-inv-optimistic MODEL_DDP=maurer-inv-optimistic-ddp NUM_CONTEXT=60 ./run_GNP_prop_68.sh

MLP Classification Experiments

See Appendix J. The numbers from the graphs can be found in eval_metrics_no_post_opt.txt (without post optimisation) eval_metrics_post_opt.txt (with post optimisation).

MODEL_NONDDP=catoni MODEL_DDP=catoni-ddp NUM_CONTEXT=30 ./run_MLP.sh
MODEL_NONDDP=kl-val MODEL_DDP=kl-val NUM_CONTEXT=30 ./run_MLP.sh

MODEL_NONDDP=catoni MODEL_DDP=catoni-ddp NUM_CONTEXT=60 ./run_MLP.sh
MODEL_NONDDP=kl-val MODEL_DDP=kl-val NUM_CONTEXT=60 ./run_MLP.sh
Owner
GitHub Repository
A fuzzing framework for SMT solvers

yinyang A fuzzing framework for SMT solvers. Given a set of seed SMT formulas, yinyang generates mutant formulas to stress-test SMT solvers. yinyang c

Project Yin-Yang for SMT Solver Testing 145 Jan 04, 2023
Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis

TDY-CNN for Text-Independent Speaker Verification Official implementation of Temporal Dynamic Convolutional Neural Network for Text-Independent Speake

Seong-Hu Kim 16 Oct 17, 2022
A Kernel fuzzer focusing on race bugs

Razzer: Finding kernel race bugs through fuzzing Environment setup $ source scripts/envsetup.sh scripts/envsetup.sh sets up necessary environment var

Systems and Software Security Lab at Seoul National University (SNU) 328 Dec 26, 2022
Face Recognition and Emotion Detector Device

Face Recognition and Emotion Detector Device Orange PI 1 Python 3.10.0 + Django 3.2.9 Project's file explanation Django manage.py Django commands hand

BootyAss 2 Dec 21, 2021
This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

haifeng xia 32 Oct 26, 2022
level1-image-classification-level1-recsys-09 created by GitHub Classroom

level1-image-classification-level1-recsys-09 ❗ 주제 설명 COVID-19 Pandemic 상황 속 마스크 착용 유무 판단 시스템 구축 마스크 착용 여부, 성별, 나이 총 세가지 기준에 따라 총 18개의 class로 구분하는 모델 ?

6 Mar 17, 2022
《A-CNN: Annularly Convolutional Neural Networks on Point Clouds》(2019)

A-CNN: Annularly Convolutional Neural Networks on Point Clouds Created by Artem Komarichev, Zichun Zhong, Jing Hua from Department of Computer Science

Artёm Komarichev 44 Feb 24, 2022
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

HEP Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior Implementation Python3 PyTorch=1.0 NVIDIA GPU+CUDA Training process The

FengZhang 34 Dec 04, 2022
Code for CVPR 2021 oral paper "Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts"

Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts The rapid progress in 3D scene understanding has come with growing dem

Facebook Research 182 Dec 30, 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 04, 2023
Plug and play transformer you can find network structure and official complete code by clicking List

Plug-and-play Module Plug and play transformer you can find network structure and official complete code by clicking List The following is to quickly

8 Mar 27, 2022
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators [Project Website] [Replicate.ai Project] StyleGAN-NADA: CLIP-Guided Domain Adaptation

992 Dec 30, 2022
MPRNet-Cloud-removal: Progressive cloud removal

MPRNet-Cloud-removal Progressive cloud removal Requirements 1.Pytorch = 1.0 2.Python 3 3.NVIDIA GPU + CUDA 9.0 4.Tensorboard Installation 1.Clone the

Semi 95 Dec 18, 2022
Short and long time series classification using convolutional neural networks

time-series-classification Short and long time series classification via convolutional neural networks In this project, we present a novel framework f

35 Oct 22, 2022
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners

DART Implementation for ICLR2022 paper Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners. Environment

ZJUNLP 83 Dec 27, 2022
Pytorch Implementation of Interaction Networks for Learning about Objects, Relations and Physics

Interaction-Network-Pytorch Pytorch Implementraion of Interaction Networks for Learning about Objects, Relations and Physics. Interaction Network is a

117 Nov 05, 2022
Repo for CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST in Tensorflow 2 Code for the paper: "CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning" by Chen Wei, Ki

Google Research 75 Nov 01, 2022
Example-custom-ml-block-keras - Custom Keras ML block example for Edge Impulse

Custom Keras ML block example for Edge Impulse This repository is an example on

Edge Impulse 8 Nov 02, 2022
Rendering color and depth images for ShapeNet models.

Color & Depth Renderer for ShapeNet This library includes the tools for rendering multi-view color and depth images of ShapeNet models. Physically bas

Yinyu Nie 41 Dec 19, 2022
TransPrompt - Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification

TransPrompt This code is implement for our EMNLP 2021's paper 《TransPrompt：Towards an Automatic Transferable Prompting Framework for Few-shot Text Cla

WangJianing 23 Dec 21, 2022