Topic Tag: Genetic Programming

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 Finite-dimensional Gaussian approximation with linear inequality constraints

 

Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensional Gaussian approach from Maatouk and Bay (2017) which can satisfy inequality conditions everywhere (either…


 Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition

    

We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor T…


 A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

 

Gaussian processes (GPs) are flexible distributions over functions that enable high-level assumptions about unknown functions to be encoded in a parsimonious, flexible and general way. Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise wh…


 Remote Sensing Image Classification with Large Scale Gaussian Processes

    

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and…


 Ensemble Multi-task Gaussian Process Regression with Multiple Latent Processes

 

Multi-task/Multi-output learning seeks to exploit correlation among tasks to enhance performance over learning or solving each task independently. In this paper, we investigate this problem in the context of Gaussian Processes (GPs) and propose a new model which learns a mixture of latent processes…


 GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

  

This work centers on the problem of stochastic filtering for systems that yield complex beliefs. The main contribution is GP-SUM, a filtering algorithm for dynamic systems expressed as Gaussian Processes (GP), that does not rely on linearizations or Gaussian approximations of the belief. The algori…


 On the Design of LQR Kernels for Efficient Controller Learning

  

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on…


 Analogical-based Bayesian Optimization

  

Some real-world problems revolve to solve the optimization problem max_{xinmathcal{X}}fleft(xright) where fleft(.right) is a black-box function and X might be the set of non-vectorial objects (e.g., distributions) where we can only define a symmetric and non-negative similarity score on it. This se…


 Geometric Semantic Genetic Programming Algorithm and Slump Prediction

Research on the performance of recycled concrete as building material in the current world is an important subject. Given the complex composition of recycled concrete, conventional methods for forecasting slump scarcely obtain satisfactory results. Based on theory of nonlinear prediction method, we…


 Slope Stability Analysis with Geometric Semantic Genetic Programming

Genetic programming has been widely used in the engineering field. Compared with the conventional genetic programming and artificial neural network, geometric semantic genetic programming (GSGP) is superior in astringency and computing efficiency. In this paper, GSGP is adopted for the classificati…