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#kernel-methods

4 curated events
papersTODAY 04:00 UTC

arXiv Paper Analyzes Resolution-Independent Encoder-Decoder Operator Learning

A new arXiv preprint examines how encoder-decoder architectures for operator learning behave as the discretization of training data changes. The authors use limiting kernels to show that the induced operator-valued kernel can be analyzed independently of the chosen finite-dimensional resolution. The work targets reliable operator learning when only finite-dimensional representations of function data are available.

research/operator-learning/arxiv/kernel-methods
papersTODAY 04:00 UTC

arXiv Paper Proposes Low-Dimensional Embeddings for Gaussian Kernels on Manifolds

A new arXiv preprint examines how Gaussian kernel similarity measures can be computed more efficiently for large sets of points. The authors build on Random Fourier Features to construct low-dimensional embeddings tailored to data lying on manifolds. The work targets applications such as kernel PCA and spectral clustering, where pairwise kernel evaluations are typically costly.

research/kernel-methods/dimensionality-reduction/arxiv
papersTODAY 04:00 UTC

Sublinear Sketches for Approximate Nearest Neighbor and Kernel Density Estimation

A revised arXiv paper proposes sublinear sketching techniques for two core machine learning problems: approximate nearest neighbor search and approximate kernel density estimation. The approach targets large-scale data analysis and information retrieval settings where exact computation is impractical, aiming to reduce memory and query costs while preserving accuracy guarantees.

research/nearest-neighbor/sketching/kernel-methods
papersSEP 10 04:00 UTC

Monograph Maps Connections Between Gaussian Processes and Kernel Hilbert Spaces

A newly updated arXiv monograph examines the relationship between two kernel-based machine learning traditions: probabilistic modeling with Gaussian processes and non-probabilistic methods built on reproducing kernel Hilbert spaces. The work lays out the mathematical connections and equivalences between the two approaches, providing a unified theoretical treatment of positive definite kernel techniques.

gaussian-processes/kernel-methods/theory/machine-learning