arXiv Statistics ML
Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation
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Laplace-Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation
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Scalable Logistic Gaussian Process Density Regression with Kinetic Langevin Sampling
The paper introduces a scalable Bayesian method for conditional density estimation using a logistic Gaussian process. It models the log conditional density with a separable covariance— a Matérn kernel on the response dimension and a Nyström-feature kernel on covariates— and samples the latent field via kinetic Langevin dynamics instead of traditional approximations. Experiments on photometric‑redshift data with up to 3.9 million observations show the approach matches state‑of‑the‑art tabular foundation models on density and calibration metrics.
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Laplace--Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation
arXiv:2606. 25169v1 Announce Type: cross Abstract: Sampling from an unnormalized target by reversing an Ornstein--Uhlenbeck diffusion requires the score of each noise-perturbed marginal.
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An Analysis of Posterior Collapse, Parameterization and Initialization in Variational Deep Gaussian Processes
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Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency Without Model Sweeps
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arXiv Machine Learning
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Exact Evaluation of the Accuracy of Diffusion Models for Inverse Problems with Gaussian Data Distributions
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