Safe Bayesian Optimization

Last update: Dec 11, 2022

Overview

SafeOpt - Safe Bayesian Optimization

This code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also provides a more scalable implementation based on [3] as well as an implementation for the original algorithm in [4]. The code can be used to automatically optimize a performance measures subject to a safety constraint by adapting parameters. The prefered way of citing this code is by referring to [1] or [2].

[1]	F. Berkenkamp, A. P. Schoellig, A. Krause, Safe Controller Optimization for Quadrotors with Gaussian Processes in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 491-496.

[2]	F. Berkenkamp, A. Krause, A. P. Schoellig, Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics, ArXiv, 2016, arXiv:1602.04450 [cs.RO].

[3]	Rikky R.P.R. Duivenvoorden, Felix Berkenkamp, Nicolas Carion, Andreas Krause, Angela P. Schoellig, Constrained Bayesian optimization with Particle Swarms for Safe Adaptive Controller Tuning, in Proc. of the IFAC (International Federation of Automatic Control) World Congress, 2017.

[4]	Y. Sui, A. Gotovos, J. W. Burdick, and A. Krause, Safe exploration for optimization with Gaussian processes in Proc. of the International Conference on Machine Learning (ICML), 2015, pp. 997–1005.

Warning: Maintenance mode

This package is no longer actively maintained. That bein said, pull requests to add functionality or fix bugs are always welcome.

Installation

The easiest way to install the necessary python libraries is by installing pip (e.g. apt-get install python-pip on Ubuntu) and running

pip install safeopt

Alternatively you can clone the repository and install it using

python setup.py install

Usage

The easiest way to get familiar with the library is to run the interactive example ipython notebooks!

Make sure that the ipywidgets module is installed. All functions and classes are documented on Read The Docs.

License

The code is licenced under the MIT license and free to use by anyone without any restrictions.

Safe Bayesian Optimization

Related tags

Overview

SafeOpt - Safe Bayesian Optimization

Warning: Maintenance mode

Installation

Usage

License

Owner

Felix Berkenkamp

Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

Official implementation of CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

A toy compiler that can convert Python scripts to pickle bytecode 🥒

Release of SPLASH: Dataset for semantic parse correction with natural language feedback in the context of text-to-SQL parsing

Efficient Training of Audio Transformers with Patchout

U-Net for GBM

Simple STAC Catalogs discovery tool.

Like ThreeJS but for Python and based on wgpu

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

Weakly supervised medical named entity classification

This project implements "virtual speed" from heart rate monito

Towards Boosting the Accuracy of Non-Latin Scene Text Recognition

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

Official code repository for the publication "Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons"

[TIP2020] Adaptive Graph Representation Learning for Video Person Re-identification

[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.

Training DALL-E with volunteers from all over the Internet using hivemind and dalle-pytorch (NeurIPS 2021 demo)

A multi-scale unsupervised learning for deformable image registration

Code accompanying "Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity," accepted to IEEE SSCI ICES 2021