Search · four archives

Results for Gaussian process regression

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

28 papers · ranked by Valyu relevance

01
BbioRxiv19 Dec 2016
A tutorial on Gaussian process regression with a focus on exploration-exploitation scenarios
Eric Schulz, Maarten Speekenbrink, Andreas Krause
This tutorial introduces the reader to Gaussian process regression as a tool to model, actively explore and exploit unknown functions. Gaussian process regression is a powerful, non-parametric Bayesian approach towards regression problems that can be utilized in exploration and exploitation scenarios. This tutorial…
93relevance
02
PPubMed31 Oct 2024
Optimizing Thermoplastic Starch Film with Heteroscedastic Gaussian Processes in Bayesian Experimental Design Framework
Gracie M. White, Amanda P. Siegel, Andres Tovar, Jacek Mateusz Bajkowski + 3 more
The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently…
93relevance
03
PPubMed9 Jan 2023
Gaussian Process Regression for Single-Channel Sound Source Localization System Based on Homomorphic Deconvolution
Keonwook Kim, Yujin Hong, Iren E. Kuznetsova
To extract the phase information from multiple receivers, the conventional sound source localization system involves substantial complexity in software and hardware. Along with the algorithm complexity, the dedicated communication channel and individual analog-to-digital conversions prevent an increase in the system’s…
92relevance
04
AarXiv2020
An Intuitive Tutorial to Gaussian Processes Regression
J. Wang
This tutorial aims to provide an intuitive understanding of the Gaussian processes regression. Gaussian processes regression (GPR) models have been widely used in machine learning applications because of their representation flexibility and inherent uncertainty measures over predictions. The basic concepts that a…
92relevance
05
AarXiv2019
The Use of Gaussian Processes in System Identification
Simo Särkkä
Gaussian processes are used in machine learning to learn input-output mappings from observed data. Gaussian process regression is based on imposing a Gaussian process prior on the unknown regressor function and statistically conditioning it on the observed data. In system identification, Gaussian processes are used to…
92relevance
06
AarXiv2015
Gaussian Processes: A Quick Introduction
Mark Ebden
If we expect the underlying function f(x) to be linear, and can make some assumptions about the input data, we might use a least-squares method to fit a straight line (linear regression). Moreover, if we suspect f(x) may also be quadratic, cubic, or even nonpolynomial, we can use the principles of model selection to…
92relevance
07
PPubMed29 Oct 2020
Kernel methods and their derivatives: Concept and perspectives for the earth system sciences Kernel methods and their derivatives
J. Emmanuel Johnson, Valero Laparra, Adrián Pérez-Suay, Miguel D. Mahecha + 2 more
Kernel methods are powerful machine learning techniques which use generic non-linear functions to solve complex tasks. They have a solid mathematical foundation and exhibit excellent performance in practice. However, kernel machines are still considered black-box models as the kernel feature mapping cannot be accessed…
90relevance
08
PPubMed21 Dec 2021
Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing
Emilio Corcione, Diana Pfezer, Mario Hentschel, Harald Giessen + 2 more
'Cristina Tarín' 'Andrea Facchinetti'] The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose…
90relevance
09
PPubMed20 Jan 2016
Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting
Miltiadis Alamaniotis, Dimitrios Bargiotas, Lefteri H. Tsoukalas
Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting…
90relevance
10
PPubMed10 Nov 2014
Estimation of Clustering Parameters Using Gaussian Process Regression Estimation of Clustering Parameters Using Gaussian Process Regression
Paul Rigby, Oscar Pizarro, Stefan B. Williams, Freddie Salsbury Jr
We propose a method for estimating the clustering parameters in a Neyman-Scott Poisson process using Gaussian process regression. It is assumed that the underlying process has been observed within a number of quadrats, and from this sparse information the distribution is modelled as a Gaussian process. The clustering…
90relevance
11
BbioRxiv4 Oct 2021
Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories
Sunny Cui, Elizabeth C. Yoo, Didong Li, Krzysztof Laudanski + 1 more
Gaussian processes (GPs) are a versatile nonparametric model for nonlinear regression and have been widely used to study spatiotemporal phenomena. However, standard GPs offer limited interpretability and generalizability for datasets with naturally occurring hierarchies. With large-scale, rapidly-updating electronic…
90relevance
12
PPubMed18 Jul 2023
Interface State Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process
Kanta Matsunaga, Takuto Harada, Shintaro Harada, Akinori Sato + 4 more
Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process Authors: ['Kanta Matsunaga' 'Takuto Harada' 'Shintaro Harada' 'Akinori Sato' 'Shota Terai' 'Mutsunori Uenuma' 'Tomoyuki Miyao' 'Yukiharu Uraoka'] During data-driven process condition optimization on…
89relevance
13
AarXiv2 Nov 2017
How priors of initial hyperparameters affect Gaussian process regression models
Zexun Chen, Bo Wang
The hyperparameters in Gaussian process regression (GPR) model with a specified kernel are often estimated from the data via the maximum marginal likelihood. Due to the non-convexity of marginal likelihood with respect to the hyperparameters, the optimization may not converge to the global maxima. A common approach to…
89relevance
14
PPubMed15 Sep 2021
The pitfalls of using Gaussian Process Regression for normative modeling The pitfalls of using Gaussian Process Regression for normative modeling
Bohan Xu, Rayus Kuplicki, Sandip Sen, Martin P. Paulus + 1 more
'Chi-Hua Chen'] Normative modeling, a group of methods used to quantify an individual’s deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across…
88relevance
15
AarXiv2022
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields
Angesom Ataklity Tesfay, Laurent Clavier
—These days we live in a world with a permanent electromagnetic field. This raises many questions about our health and the deployment of new equipment. The problem is that these fields remain difficult to visualize easily, which only some experts can understand. To tackle this problem, we propose to spatially estimate…
88relevance
16
BbioRxiv11 May 2021
The pitfalls of using Gaussian Process Regression for normative modeling
Bohan Xu, Rayus Kuplicki, Sandip Sen, Martin P. Paulus
Normative modeling, a group of methods used to quantify an individual’s deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input…
86relevance
17
AarXiv2020
Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference
Anna Mateo-Sanchis, Jordi Muñoz-Marı́, Adrián Pérez-Suay, Gustau Camps‐Valls
'Gustau Camps‐Valls'] Abstract—This paper introduces warped Gaussian processes (WGP) regression in remote sensing applications. WGP models output observations as a parametric nonlinear transformation of a GP. The parameters of such prior model are then learned via standard maximum likelihood. We show the good…
86relevance
18
BbioRxiv30 Jul 2020
Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments
Nuha BinTayyash, Sokratia Georgaka, ST John, Sumon Ahmed + 3 more
The negative binomial distribution is a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modeling temporal or spatial changes in gene expression. However, currently available GP regression methods that…
85relevance
19
BbioRxiv15 Jul 2025
Gaussian process forecasting of sparse ecological time series
Parul V. Patil, Robert B. Gramacy, Leah R. Johnson
Ecological time series are often unevenly sampled in time. That is, because the sampling processes used are resource intensive, data may be collected infrequently, or with adaptive frequencies triggered by presence of a target variable. When the data are irregularly spaced, standard time series methods may not be…
84relevance
20
BbioRxiv25 May 2016
Inferring time-derivatives, including cell growth rates, using Gaussian processes
Peter S. Swain, Keiran Stevenson, Allen Leary, Luis F. Montano-Gutierrez + 3 more
Often the time-derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time-derivatives as a…
83relevance
21
CchemRxiv25 Mar 2024
Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
78relevance
22
CchemRxiv25 Mar 2025
Capturing Variability in Material Property Predictions for Plastics Recycling via Machine Learning
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…
77relevance
23
CchemRxiv6 Jul 2022
Gaussian Processes for the Analysis of Electrochemical Impedance Spectroscopy Data: Prediction, Filtering, and Active Learning
Baptiste Py, Adeleke Maradesa, Francesco Ciucci
Electrochemical impedance spectroscopy (EIS) is a widespread characterization technique used to study electrochemical systems. However, several shortcomings still limit the application of this technique. First, EIS data is intrinsically noisy, hindering spectra regression and prediction at unknown frequencies. Second…
77relevance
24
CchemRxiv7 Jul 2023
Designing electrodes and electrolytes for batteries by leveraging deep learning
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
75relevance
25
CchemRxiv23 Jun 2021
Bayesian optimization of nanoporous materials
Aryan Deshwal, Cory Simon, Janardhan Rao Doppa
Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate…
75relevance
26
CchemRxiv30 May 2023
Using GPT-4 in Parameter Selection of Materials Informatics: Improving Predictive Accuracy Amidst Data Scarcity and 'Ugly Duckling' Dilemma
Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi + 1 more
Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current…
65relevance
27
CchemRxiv25 Apr 2025
Generative machine learning approaches to optimization
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root ﬁnding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
64relevance
28
CchemRxiv8 Apr 2025
Solving an inverse problem with generative models
Authors not listed
Inverse problems, where we seek the values of inputs to a model that lead to a desired set of outputs, are a challenges subset of problems in science and engineering. In this work we demonstrate the use of two generative AI methods to solve inverse problems. We compare this approach to two more conventional approaches…
54relevance

Results for Gaussian process regression

A tutorial on Gaussian process regression with a focus on exploration-exploitation scenarios

Optimizing Thermoplastic Starch Film with Heteroscedastic Gaussian Processes in Bayesian Experimental Design Framework

Gaussian Process Regression for Single-Channel Sound Source Localization System Based on Homomorphic Deconvolution

An Intuitive Tutorial to Gaussian Processes Regression

The Use of Gaussian Processes in System Identification

Gaussian Processes: A Quick Introduction

Kernel methods and their derivatives: Concept and perspectives for the earth system sciences Kernel methods and their derivatives

Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting

Estimation of Clustering Parameters Using Gaussian Process Regression Estimation of Clustering Parameters Using Gaussian Process Regression

Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories

Interface State Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process

How priors of initial hyperparameters affect Gaussian process regression models

The pitfalls of using Gaussian Process Regression for normative modeling The pitfalls of using Gaussian Process Regression for normative modeling

Gaussian Process-based Spatial Reconstruction of Electromagnetic fields

The pitfalls of using Gaussian Process Regression for normative modeling

Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

Gaussian process forecasting of sparse ecological time series

Inferring time-derivatives, including cell growth rates, using Gaussian processes

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Capturing Variability in Material Property Predictions for Plastics Recycling via Machine Learning

Gaussian Processes for the Analysis of Electrochemical Impedance Spectroscopy Data: Prediction, Filtering, and Active Learning

Designing electrodes and electrolytes for batteries by leveraging deep learning

Bayesian optimization of nanoporous materials

Using GPT-4 in Parameter Selection of Materials Informatics: Improving Predictive Accuracy Amidst Data Scarcity and 'Ugly Duckling' Dilemma

Generative machine learning approaches to optimization

Solving an inverse problem with generative models

A tutorial on Gaussian process regression with a focus on exploration-exploitation scenarios

Optimizing Thermoplastic Starch Film with Heteroscedastic Gaussian Processes in Bayesian Experimental Design Framework

Gaussian Process Regression for Single-Channel Sound Source Localization System Based on Homomorphic Deconvolution

An Intuitive Tutorial to Gaussian Processes Regression

The Use of Gaussian Processes in System Identification

Gaussian Processes: A Quick Introduction

Kernel methods and their derivatives: Concept and perspectives for the earth system sciences Kernel methods and their derivatives

Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting

Estimation of Clustering Parameters Using Gaussian Process Regression Estimation of Clustering Parameters Using Gaussian Process Regression

Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories

Interface State Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process

How priors of initial hyperparameters affect Gaussian process regression models

The pitfalls of using Gaussian Process Regression for normative modeling The pitfalls of using Gaussian Process Regression for normative modeling

Gaussian Process-based Spatial Reconstruction of Electromagnetic fields

The pitfalls of using Gaussian Process Regression for normative modeling

Warped Gaussian Processes in Remote Sensing Parameter Estimation and Causal Inference

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

Gaussian process forecasting of sparse ecological time series

Inferring time-derivatives, including cell growth rates, using Gaussian processes

Beyond Molecular Structure: Critically Assessing Machine Learning for Designing Organic Photovoltaic Materials and Devices

Capturing Variability in Material Property Predictions for Plastics Recycling via Machine Learning

Gaussian Processes for the Analysis of Electrochemical Impedance Spectroscopy Data: Prediction, Filtering, and Active Learning

Designing electrodes and electrolytes for batteries by leveraging deep learning

Bayesian optimization of nanoporous materials

Using GPT-4 in Parameter Selection of Materials Informatics: Improving Predictive Accuracy Amidst Data Scarcity and 'Ugly Duckling' Dilemma

Generative machine learning approaches to optimization

Solving an inverse problem with generative models