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Found 7 papers, 7 papers with code
Permutation-Free High-Order Interaction Tests
Kernel-based hypothesis tests offer a flexible, non-parametric tool to detect high-order interactions in multivariate data, beyond pairwise relationships.
Information-Theoretic Measures on Lattices for High-Order Interactions
Traditional measures based solely on pairwise associations often fail to capture the complex statistical structure of multivariate data.
Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data.
Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions
Models that rely solely on pairwise relationships often fail to capture the complete statistical structure of the complex multivariate data found in diverse domains, such as socio-economic, ecological, or biomedical systems.
Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Series
Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas.
Interpretable statistical representations of neural population dynamics and geometry
The dynamics of neuron populations commonly evolve on low-dimensional manifolds.
Semi-supervised classification on graphs using explicit diffusion dynamics
Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples.
