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Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage
In the machine learning domain, active learning is an iterative data sel...
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Remarks on multivariate Gaussian Process
Gaussian process occupies one of the leading places in modern Statistics...
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A Framework for Evaluating Approximation Methods for Gaussian Process Regression
Gaussian process (GP) predictors are an important component of many Baye...
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Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach
Quantification and minimization of uncertainty is an important task in t...
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Actively Learning Gaussian Process Dynamics
Despite the availability of ever more data enabled through modern sensor...
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Learning the Pareto Front with Hypernetworks
Multi-objective optimization problems are prevalent in machine learning....
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SAMBA: Safe Model-Based Active Reinforcement Learning
In this paper, we propose SAMBA, a novel framework for safe reinforcemen...
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Projection based Active Gaussian Process Regression for Pareto Front Modeling
Pareto Front (PF) modeling is essential in decision making problems across all domains such as economics, medicine or engineering. In Operation Research literature, this task has been addressed based on multi-objective optimization algorithms. However, without learning models for PF, these methods cannot examine whether a new provided point locates on PF or not. In this paper, we reconsider the task from Data Mining perspective. A novel projection based active Gaussian process regression (P- aGPR) method is proposed for efficient PF modeling. First, P- aGPR chooses a series of projection spaces with dimensionalities ranking from low to high. Next, in each projection space, a Gaussian process regression (GPR) model is trained to represent the constraint that PF should satisfy in that space. Moreover, in order to improve modeling efficacy and stability, an active learning framework has been developed by exploiting the uncertainty information obtained in the GPR models. Different from all existing methods, our proposed P-aGPR method can not only provide a generative PF model, but also fast examine whether a provided point locates on PF or not. The numerical results demonstrate that compared to state-of-the-art passive learning methods the proposed P-aGPR method can achieve higher modeling accuracy and stability.
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