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Found 10 papers, 0 papers with code
Active Learning of Piecewise Gaussian Process Surrogates
Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisition schemes in machine learning.
Gaussian Process Model for Estimating Piecewise Continuous Regression Functions
To accommodate the possibilities of the local data from different regions, the local data is partitioned into two sides by a local linear boundary, and only the local data belonging to the same side as the test location is used for the regression estimate.
Data Science for Motion and Time Analysis with Modern Motion Sensor Data
Unsolved technical questions include: How the motion and time information can be extracted from the motion sensor data, how work motions and execution rates are statistically modeled and compared, and what are the statistical correlations of motions to the rates?
Variable selection for Gaussian process regression through a sparse projection
The sparse projection matrix is considered as an unknown parameter.
Robust Gaussian Process Regression with a Bias Model
This paper presents a new approach to a robust Gaussian process (GP) regression.
Sequential Adaptive Design for Jump Regression Estimation
Motivated by two scientific examples, this paper presents a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous.
Patchwork Kriging for Large-scale Gaussian Process Regression
Unlike existing local partitioned GP approaches, we introduce a technique for patching together the local GP models nearly seamlessly to ensure that the local GP models for two neighboring regions produce nearly the same response prediction and prediction error variance on the boundary between the two regions.
Two-Level Structural Sparsity Regularization for Identifying Lattices and Defects in Noisy Images
We propose to formulate the identification of the lattice groups as a sparse group selection problem.
Robust Regression For Image Binarization Under Heavy Noises and Nonuniform Background
This paper presents a robust regression approach for image binarization under significant background variations and observation noises.
Sparse Filtered SIRT for Electron Tomography
This paper presents a new approach to largely mitigate the effect of noises and missing wedges.
