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gaussian-processes

topic4 events
papersTODAY 04:00 UTC

Operator-Informed Gaussian Processes Model Complex Helmholtz Wavefields

Researchers present an approach that embeds knowledge of the Helmholtz operator into Gaussian process models so they can reconstruct complex-valued wavefields, including the dissipative case where the squared wavenumber becomes complex. The method is tested first on synthetic benchmarks and then applied to in vivo brain elastography, where it infers tissue properties from limited, noisy measurements. The work emphasizes uncertainty quantification alongside field estimation.

gaussian-processes/medical-imaging/elastography/research
papersTODAY 04:00 UTC

Differential Privacy of Gaussian Process Posterior Sampling

This paper studies the privacy guarantees of releasing posterior sample paths from a Gaussian process when the entire training set, including covariates and responses, is considered private. Rather than relying on standard differential-privacy mechanisms that add external noise, the analysis focuses on the randomness inherent in posterior sampling itself. It provides a formal treatment of how much privacy such released sample paths preserve.

differential-privacy/gaussian-processes/privacy/research
papersTODAY 04:00 UTC

Bayesian optimization with kernel ensembles for acoustic source localization

A new arXiv preprint proposes using Bayesian optimization to jointly estimate source location and seabed geoacoustic parameters, a task that normally demands many evaluations of an costly normal-mode propagation model. Instead of a single Gaussian process surrogate, the method combines an ensemble of kernels and selects the next sampling point based on disagreement among them. The authors report accurate parameter estimates while keeping the number of expensive model runs low.

research/bayesian-optimization/acoustics/gaussian-processes
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