Gaussian Processes
PulseAugur coverage of Gaussian Processes — every cluster mentioning Gaussian Processes across labs, papers, and developer communities, ranked by signal.
- 2026-05-20 research_milestone A new paper proposes a method to condition Gaussian Processes on natural language and other complex data. source
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New R-MOGP method offers outlier-robust Gaussian process modeling
Researchers have developed a new method called R-MOGP to address the sensitivity of Multi-Output Gaussian Processes (MOGPs) to outliers. This novel approach ensures robustness against anomalous data points in any indivi…
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Physics-Informed ML May Cut Carbon Footprints in Structural Monitoring
A new research paper explores the environmental impact of physics-informed machine learning (PIML) in structural health monitoring. The study compares the carbon footprint of four PIML approaches against traditional bla…
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New Riemannian geometry for Sinkhorn divergence established
Researchers have developed a new framework for understanding the geometry of Sinkhorn divergence, a widely used measure in optimal transport. This work establishes a formal Riemannian structure and derives key geometric…
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PYTHIA framework uses transfer learning for material optimization
Researchers have introduced PYTHIA, a novel machine learning framework designed to optimize material properties like defects and adsorption sites. PYTHIA leverages foundational models and transfer learning, utilizing a …
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New Continuous-Depth Formulation Enhances Deep Gaussian Processes
Researchers have introduced a novel continuous-depth formulation for Deep Gaussian Processes (DGPs) to address optimization challenges associated with discrete-depth models. This new approach treats depth as a continuou…
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Machine learning models benchmarked for atmospheric seeing forecasting
Researchers have evaluated machine learning models for short-term atmospheric seeing forecasting, aiming to improve astronomical observations and optical communication. The study compared Gaussian Processes (GPs) and a …
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New Bayesian Model Identifies Financial Regimes in Time Series Data
Researchers have introduced the Time-partitioning Gaussian Process Mixture Model (TPGPMM), a novel Bayesian nonparametric framework designed to identify persistent regimes and model nonstationary dependencies within tim…
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New framework merges Gaussian Processes and diffusion models for scientific inference
Researchers have developed a novel framework that merges Gaussian Processes (GPs) with diffusion models to enhance scientific inference. This approach combines the principled uncertainty quantification of GPs with the g…
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New framework uses Hierarchical Gaussian Processes for survey weighting
Researchers have developed a new survey weighting framework utilizing hierarchical Gaussian process (HGP) regression. This method combines structured group-level effects with kernel-based smoothing to improve informatio…
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AI benchmark ActMOF accelerates metal-organic framework synthesis
Researchers have developed ActMOF, a new benchmark for comparing active learning strategies in the synthesis of metal-organic frameworks (MOFs). This system uses experimentally measured data from aluminum-carboxylate MO…
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New Deep Jump Gaussian Processes method enhances high-dimensional modeling
Researchers have introduced Deep Jump Gaussian Processes (DJGP), a new method designed for modeling complex functions in high-dimensional spaces. This approach improves upon existing Jump Gaussian Processes by integrati…
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New method improves Gaussian approximation for wide neural networks
Researchers have developed a method to improve the convergence rate of Gaussian approximations for wide neural networks. This new technique lifts finite-sample bounds to functional bounds, achieving a convergence rate o…
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New paper offers strong Gaussian approximations for SGD
This paper introduces strong Gaussian approximations for constant learning rate stochastic gradient descent (SGD), particularly for heavy-tailed noise and when the number of iterates grows while the learning rate shrink…
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New Bayesian Model MEGPODE Enhances Dynamical System Analysis
Researchers have introduced MEGPODE, a novel Bayesian nonparametric mixed-effect ODE model designed for complex dynamical systems. This model decomposes subject-specific vector fields into a shared population component …
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New CUTS-GPR method enables scalable Gaussian Process Regression
Researchers have developed CUTS-GPR, a novel method for performing Gaussian Process Regression (GPR) in high-dimensional and incomplete grid settings. This technique significantly improves the scalability of kernel matr…
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New ISBO framework offers scalable Bayesian Optimization for spatio-temporal data
Researchers have developed ISBO, a novel Bayesian Optimization framework designed for scalable spatio-temporal data analysis. This approach models log-intensity using Log-Gaussian Cox Processes and employs the INLA-SPDE…
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New 'ghost tasking' method enhances Gaussian processes for differential equations
Researchers have introduced a novel procedure called "ghost tasking" to enhance the capabilities of parametrized Gaussian processes, particularly for solving linear differential equations in data-scarce environments. Th…
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New theory sharpens Gaussian Process kernel estimation asymptotics
This paper provides a comprehensive asymptotic theory for the maximum likelihood estimators (MLEs) of Gaussian Process (GP) kernel parameters, specifically focusing on the radial basis function (RBF) kernel. Researchers…
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Deep Ensemble improves AI path planning for oil spill monitoring
Researchers have developed a new method for informative path planning in aquatic environmental monitoring, specifically for scenarios like oil spills. This approach replaces traditional Gaussian Processes with a Deep En…
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New research explores advanced Bayesian optimization techniques · 6 sources tracked
Recent research published on arXiv explores advancements in Bayesian optimization (BO), a technique for optimizing expensive black-box functions. Several papers introduce new algorithms and theoretical frameworks to imp…