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

Last update: Dec 21, 2021

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

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

Related tags

Overview

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

Installation

Generating Datasets

Theory Experiments

GNP Classification Experiments

MLP Classification Experiments

Owner

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Automate issue discovery for your projects against Lightning nightly and releases.

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ZEBRA: Zero Evidence Biometric Recognition Assessment

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Acute ischemic stroke dataset

Arabic Car License Recognition. A solution to the kaggle competition Machathon 3.0.

Learning to Simulate Dynamic Environments with GameGAN (CVPR 2020)

Make a surveillance camera from your raspberry pi!

Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Code for "The Box Size Confidence Bias Harms Your Object Detector"

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Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021.

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The personal repository of the work: DanceNet3D: Music Based Dance Generation with Parametric Motion Transformer.

Neural Network Libraries

Official Repo of my work for SREC Nandyal Machine Learning Bootcamp

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

Related tags

Overview

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

Installation

Generating Datasets

Theory Experiments

GNP Classification Experiments

MLP Classification Experiments

Owner

This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).

Capture all information throughout your model's development in a reproducible way and tie results directly to the model code!

Automate issue discovery for your projects against Lightning nightly and releases.

Joint parameterization and fitting of stroke clusters

Animal Sound Classification (Cats Vrs Dogs Audio Sentiment Classification)

Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations"

ZEBRA: Zero Evidence Biometric Recognition Assessment

Hyperparameter tuning for humans

Acute ischemic stroke dataset

Arabic Car License Recognition. A solution to the kaggle competition Machathon 3.0.

Learning to Simulate Dynamic Environments with GameGAN (CVPR 2020)

Make a surveillance camera from your raspberry pi!

Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Code for "The Box Size Confidence Bias Harms Your Object Detector"

Spectral Tensor Train Parameterization of Deep Learning Layers

Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021.

Decensoring Hentai with Deep Neural Networks. Formerly named DeepMindBreak.

The personal repository of the work: *DanceNet3D: Music Based Dance Generation with Parametric Motion Transformer*.

Neural Network Libraries

Official Repo of my work for SREC Nandyal Machine Learning Bootcamp

The personal repository of the work: DanceNet3D: Music Based Dance Generation with Parametric Motion Transformer.