scholarly journals Improved Gaussian Process Acquisition for Targeted Bayesian Optimization

2021 ◽  
pp. 12-18
Author(s):  
Peter Mitic ◽  
Keyword(s):  
Gaussian Process ◽  
Optimization Problem ◽  
Bayesian Optimization ◽  
Stochastic Algorithm ◽  
Confidence Bound ◽  
Noise Component ◽  
Simple Modification ◽  
Lower Confidence ◽  
Lower Confidence Bound ◽  
Long Time

A black-box optimization problem is considered, in which the function to be optimized can only be expressed in terms of a complicated stochastic algorithm that takes a long time to evaluate. The value returned is required to be sufficiently near to a target value, and uses data that has a significant noise component. Bayesian Optimization with an underlying Gaussian Process is used as an optimization solution, and its effectiveness is measured in terms of the number of function evaluations required to attain the target. To improve results, a simple modification of the Gaussian Process ‘Lower Confidence Bound’ (LCB) acquisition function is proposed. The expression used for the confidence bound is squared in order to better comply with the target requirement. With this modification, much improved results compared to random selection methods and to other commonly used acquisition functions are obtained.

scholarly journals Multi-Task Gaussian Process Upper Confidence Bound for Hyperparameter Tuning

2021 ◽  
Author(s):  
Bo Shen ◽  
Raghav Gnanasambandam ◽  
Rongxuan Wang ◽  
Zhenyu Kong
Keyword(s):  
Gaussian Process ◽  
Black Box ◽  
Bayesian Optimization ◽  
Query Point ◽  
Confidence Bound ◽  
Bayesian Optimization Algorithm ◽  
Machine Learning Model ◽  
Step Algorithm ◽  
Upper Confidence Bound ◽  
General Method

In many scientific and engineering applications, Bayesian optimization (BO) is a powerful tool for hyperparameter tuning of a machine learning model, materials design and discovery, etc. BO guides the choice of experiments in a sequential way to find a good combination of design points in as few experiments as possible. It can be formulated as a problem of optimizing a “black-box” function. Different from single-task Bayesian optimization, Multi-task Bayesian optimization is a general method to efficiently optimize multiple different but correlated “black-box” functions. The previous works in Multi-task Bayesian optimization algorithm queries a point to be evaluated for all tasks in each round of search, which is not efficient. For the case where different tasks are correlated, it is not necessary to evaluate all tasks for a given query point. Therefore, the objective of this work is to develop an algorithm for multi-task Bayesian optimization with automatic task selection so that only one task evaluation is needed per query round. Specifically, a new algorithm, namely, multi-task Gaussian process upper confidence bound (MT-GPUCB), is proposed to achieve this objective. The MT-GPUCB is a two-step algorithm, where the first step chooses which query point to evaluate, and the second step automatically selects the most informative task to evaluate. Under the bandit setting, a theoretical analysis is provided to show that our proposed MT-GPUCB is no-regret under some mild conditions. Our proposed algorithm is verified experimentally on a range of synthetic functions as well as real-world problems. The results clearly show the advantages of our query strategy for both design point and task.

scholarly journals Analysis of the specificity of the SD Biosensor Standard Q Ag-Test based on Slovak mass testing data

2020 ◽  
Author(s):  
Michal Hledík ◽  
Jitka Polechová ◽  
Mathias Beiglböck ◽  
Anna Nele Herdina ◽  
Robert Strassl ◽  
...  
Keyword(s):  
False Positives ◽  
Confidence Bound ◽  
Worst Case ◽  
Lower Confidence ◽  
Lower Confidence Bound ◽  
Testing Data ◽  
Worst Case Approach

AbstractFrom 31.10. - 1.11.2020 Slovakia has used the SD Biosensor Standard Q Ag-Test for nationwide tests for SARS-CoV-2, in which 3,625,332 persons from 79 counties were tested. Based on this data, we calculate that the specificity of the test is at least 99.6% (with a 97.5% one-sided lower confidence bound). Our analysis is based on a worst case approach in which all positives are assumed to be false positives. Therefore, the actual specificity is expected to exceed 99.6%.

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