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Found 8 papers, 1 papers with code
SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embedding
Our framework offers a simple but comprehensive way to understand the underlying patterns and trends in dynamic KG, thereby enhancing the accuracy of predictions and the relevance of recommendations.
Gaussian Process Regression under Computational and Epistemic Misspecification
Gaussian process regression is a classical kernel method for function estimation and data interpolation.
Alignment of Density Maps in Wasserstein Distance
In this paper we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy.
Optimization on Manifolds via Graph Gaussian Processes
We rely on a point cloud of manifold samples to define a graph Gaussian process surrogate model for the objective.
Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning
In recent decades, science and engineering have been revolutionized by a momentous growth in the amount of available data.
Uniform Consistency in Nonparametric Mixture Models
We study uniform consistency in nonparametric mixture models as well as closely related mixture of regression (also known as mixed regression) models, where the regression functions are allowed to be nonparametric and the error distributions are assumed to be convolutions of a Gaussian density.
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective
In this paper we analyze the graph-based approach to semi-supervised learning under a manifold assumption.
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis
Several data analysis techniques employ similarity relationships between data points to uncover the intrinsic dimension and geometric structure of the underlying data-generating mechanism.
