Safe Bayesian Optimization

Related tags

Deep Learningreinforcement-learningroboticsoptimizationsafetygaussian-processes
Overview

SafeOpt - Safe Bayesian Optimization

Build Status Documentation Status

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].

Youtube video
[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This package is no longer actively maintained.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.

Owner
Felix Berkenkamp
Research Scientist
Felix Berkenkamp
GitHub Repository
Official PyTorch implementation of RobustNet (CVPR 2021 Oral)

RobustNet (CVPR 2021 Oral): Official Project Webpage Codes and pretrained models will be released soon. This repository provides the official PyTorch

Sungha Choi 173 Dec 21, 2022
Official Implementation for Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation We present a generic image-to-image translation framework, pixel2style2pixel (pSp

2.8k Dec 30, 2022
Official implementation of the paper 'Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution' in CVPR 2022

LDL Paper | Supplementary Material Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution Jie Liang*, Hu

150 Dec 26, 2022
Multi Agent Path Finding Algorithms

MATP-solver Simulator collision check path step random initial states or given states Traditional method Seperate A* algorithem Confict-based Search S

30 Dec 12, 2022
Home for cuQuantum Python & NVIDIA cuQuantum SDK C++ samples

Welcome to the cuQuantum repository! This public repository contains two sets of files related to the NVIDIA cuQuantum SDK: samples: All C/C++ sample

NVIDIA Corporation 147 Dec 27, 2022
Machine learning for NeuroImaging in Python

nilearn Nilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive doc

919 Dec 25, 2022
How Effective is Incongruity? Implications for Code-mix Sarcasm Detection.

Code for the paper: How Effective is Incongruity? Implications for Code-mix Sarcasm Detection - ICON ACL 2021

2 Jun 05, 2022
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

Soubhik Sanyal 71 Dec 30, 2022
Data stream analytics: Implement online learning methods to address concept drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" accepted in IEEE GlobeCom 2021.

PWPAE-Concept-Drift-Detection-and-Adaptation This is the code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT

Western OC2 Lab 162 Dec 16, 2022
PyTorch implementation of neural style randomization for data augmentation

README Augment training images for deep neural networks by randomizing their visual style, as described in our paper: https://arxiv.org/abs/1809.05375

84 Nov 23, 2022
Run PowerShell command without invoking powershell.exe

PowerLessShell PowerLessShell rely on MSBuild.exe to remotely execute PowerShell scripts and commands without spawning powershell.exe. You can also ex

Mr.Un1k0d3r 1.2k Jan 03, 2023
Official implementation of Long-Short Transformer in PyTorch.

Long-Short Transformer (Transformer-LS) This repository hosts the code and models for the paper: Long-Short Transformer: Efficient Transformers for La

NVIDIA Corporation 198 Dec 29, 2022
ConformalLayers: A non-linear sequential neural network with associative layers

ConformalLayers: A non-linear sequential neural network with associative layers ConformalLayers is a conformal embedding of sequential layers of Convo

Prograf-UFF 5 Sep 28, 2022
FedScale: Benchmarking Model and System Performance of Federated Learning

FedScale: Benchmarking Model and System Performance of Federated Learning (Paper) This repository contains scripts and instructions of building FedSca

268 Jan 01, 2023
The repo of Feedback Networks, CVPR17

Feedback Networks http://feedbacknet.stanford.edu/ Paper: Feedback Networks, CVPR 2017. Amir R. Zamir*,Te-Lin Wu*, Lin Sun, William B. Shen, Bertram E

Stanford Vision and Learning Lab 87 Nov 19, 2022
Out-of-boundary View Synthesis towards Full-frame Video Stabilization

Out-of-boundary View Synthesis towards Full-frame Video Stabilization Introduction | Update | Results Demo | Introduction This repository contains the

25 Oct 10, 2022
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation (CVPR'21)

EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation (CVPR'21) Citation If y

addisonwang 18 Nov 11, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection

Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection Introduction This repository includes codes and models of "Effect of De

Amir Abbasi 5 Sep 05, 2022
Some methods for comparing network representations in deep learning and neuroscience.

Generalized Shape Metrics on Neural Representations In neuroscience and in deep learning, quantifying the (dis)similarity of neural representations ac

Alex Williams 45 Dec 27, 2022