25 papers · ranked by Valyu relevance
Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea + 1 more
'Thomas Pinder'] Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming…
Fabian Berns, Jan Hüwel, Christian Beecks
Gaussian process models (GPMs) are widely regarded as a prominent tool for learning statistical data models that enable interpolation, regression, and classification. These models are typically instantiated by a Gaussian Process with a zero-mean function and a radial basis covariance function. While these default…
Somya Sharma, Snigdhansu Chatterjee, Eric Nalisnick, Dustin Tran
With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. We provide a…
Tilekbek Zhoroev, Emily F. Hamilton, Philip A. Warrick, Luca Mesin
Clinicians routinely perform pelvic examinations to assess the progress of labor. Clinical guidelines to interpret these examinations, using time-based models of cervical dilation, are not always followed and have not contributed to reducing cesarean-section rates. We present a novel Gaussian process model of labor…
Ekaterina Noskova, Viacheslav Borovitskiy, A Kern
Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model’s parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware…
Allen Ross, Jason Lloyd-Price, Ali Rahnavard
Identifying meaningful associations from small-sample longitudinal data is challenging, especially in low signal-to-noise environments where the Gaussian likelihood assumption does not hold. We introduce two methods to algorithmically perform variable selection with sparse, irregularly sampled, longitudinal count data…
Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann
— Model Predictive Control evolved as the state of the art paradigm for safety critical control tasks. Controlas-Inference approaches thereof model the constrained optimization problem as a probabilistic inference problem. The constraints have to be implemented into the inference model. A recently introduced…
Eldon A. Gunn, Nikhil Sengupta, Ben Swallow
software package and GPUs Authors: ['Eldon A. Gunn' 'Nikhil Sengupta' 'Ben Swallow'] Gaussian process are a widely-used statistical tool for conducting non-parametric inference in applied sciences, with many computational packages available to fit to data and predict future observations. We study the use of the Greta…
Anis Fradi, Tien-Tam Tran, Chafik Samir, Robertas Alzbutas + 2 more
'Mark Girolami' 'Hussein Rappel'] In this paper, we address the challenges of inferring and learning from a substantial number of observations ( $N≫1$) with a Gaussian process regression model. First, we propose a flexible construction of well-adapted covariances originally derived from specific differential operators.…
Till Hoffmann, Jukka‐Pekka Onnela
Gaussian processes (GPs) are sophisticated distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. We implement two methods for scaling GP inference in Stan: First, a general sparse approximation using a directed acyclic dependency graph…
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…
Juan J. L. Velázquez, Luis A. Escamilla, Purba Mukherjee, J. Alberto Vázquez
Processes Regression Authors: ['Juan J. L. Velázquez' 'Luis A. Escamilla' 'Purba Mukherjee' 'J. Alberto Vázquez'] The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine…
Galvis-Florez, Cristian A., Farooq Ahmad, Särkkä + 1 more
—Probabilistic machine learning models are distinguished by their ability to integrate prior knowledge of noise statistics, smoothness parameters, and training data uncertainty. A common approach involves modeling data with Gaussian processes; however, their computational complexity quickly becomes intractable as the…
Didong Li, Wenpin Tang, Sudipto Banerjee
Gaussian processes are widely employed as versatile modelling and predictive tools in spatial statistics, functional data analysis, computer modelling and diverse applications of machine learning. They have been widely studied over Euclidean spaces, where they are specified using covariance functions or covariograms…
Vasiliki D. Agou, Andrew Pavlides, Dionissios T. Hristopulos, Antonio M. Scarfone
'Antonio M. Scarfone'] Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex…
Elio Nushi, François P. Douillard, Katja Selby, Miia Lindström + 1 more
In this study, we introduce a Bayesian model-based method for clustering transcriptomics time series data with multiple replicates. This technique is based on sampling Gaussian processes (GPs) within an infinite mixture model from a Dirichlet process (DP). Our method uses multiple GP models to accommodate for multiple…
M. Rule, P. Chaudhuri-Vayalambrone, M. Krstulovic, M. Bauza + 2 more
We present practical solutions to applying Gaussian-process methods to calculate spatial statistics for grid cells in large environments. Gaussian processes are a data efficient approach to inferring neural tuning as a function of time, space, and other variables. We discuss how to design appropriate kernels for grid…
M. Nicolás Cruz-Bournazou, Harini Narayanan, Alessandro Fagnani, Alessandro Butté
Hybrid modeling, meaning the integration of data-driven and knowledge-based methods, is quickly gaining popularity among many research fields, including bioprocess engineering and development. Recently, the data-driven part of hybrid methods have been largely extended with machine learning algorithms (e.g., artificial…
Swapnil Mishra, Seth Flaxman, Tresnia Berah, Harrison Zhu + 2 more
\usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi $$\end{document} π VAE: a stochastic process prior for Bayesian deep learning with MCMC Authors: ['Swapnil Mishra'…
Eric J. Ward, Sean C. Anderson
1. Spatial and spatiotemporal models are increasingly used in ecology for a range of purposes, such as tracking population change, assessing species distributions, and modelling spatial processes. Many models, including generalized additive models (GAMs) and Gaussian random fields (fit via the Stochastic Partial…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
Adeleke Maradesa, Baptiste Py, Francesco Ciucci
Electrochemical impedance spectroscopy (EIS) is widely used to study the properties of electrochemical materials and systems. However, analyzing EIS data remains challenging. Among various analysis methods, the distribution of relaxation times (DRT) has emerged as a novel non-parametric approach capable of providing…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…