Equivalence between Gaussian processes and linear diffusion models enables general conditioning on arbitrary pointwise likelihoods via ODE dynamics and Monte Carlo guidance approximation.
Gaussian Processes for Big Data
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
abstract
We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be vari- ationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our ap- proach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.
verdicts
UNVERDICTED 10representative citing papers
SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.
A logit-space SVGP framework explicitly models annotator bias and variance to improve uncertainty calibration in multi-rater probabilistic segmentation while keeping accuracy comparable to prior methods.
VH-CBM uses a Gaussian process in VLM embedding space to propagate sparse human annotations and improve concept accuracy and calibration over pure VLM-guided concept bottleneck models.
A two-stage posterior-weighted Gaussian Process generates approximately periodic time series by keeping an identical mean function across repetitions while permitting smooth inter-repetition variation.
POGPN-JPSS integrates partially observable Gaussian process networks with joint parameter and state-space modeling to leverage expert-derived low-dimensional features from high-dimensional intermediate observations for faster optimization of multi-stage manufacturing processes.
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
A dual-ranking strategy improves offline data-driven multi-objective optimization by prioritizing solutions that score well on both predicted performance and low uncertainty across different surrogate models.
Empirical study finds GPs often superior to TabPFN for UQ and accuracy in data-scarce tabular regression, with TabPFN competitive in complex high-dimensional high-data regimes.
Implements ASMC in GEBHM to scale GP hyperparameter estimation to large datasets, saving time while maintaining predictability.
citing papers explorer
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Conditioning Gaussian Processes on Almost Anything
Equivalence between Gaussian processes and linear diffusion models enables general conditioning on arbitrary pointwise likelihoods via ODE dynamics and Monte Carlo guidance approximation.
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Rates of forgetting for the sequentially Markov coalescent
SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.
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Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability
A logit-space SVGP framework explicitly models annotator bias and variance to improve uncertainty calibration in multi-rater probabilistic segmentation while keeping accuracy comparable to prior methods.
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Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
VH-CBM uses a Gaussian process in VLM embedding space to propagate sparse human annotations and improve concept accuracy and calibration over pure VLM-guided concept bottleneck models.
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Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process
A two-stage posterior-weighted Gaussian Process generates approximately periodic time series by keeping an identical mean function across repetitions while permitting smooth inter-repetition variation.
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Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization
POGPN-JPSS integrates partially observable Gaussian process networks with joint parameter and state-space modeling to leverage expert-derived low-dimensional features from high-dimensional intermediate observations for faster optimization of multi-stage manufacturing processes.
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A Cubing Strategy for Identifying Stable Hyperparameter Regions for Uncertainty Quantification in Spatial Deep Learning
A recursive cubing framework identifies stable hyperparameter regions for MC dropout uncertainty quantification in spatial deep learning and produces competitive or superior predictive intervals versus a statistical baseline on simulations and land-surface temperature data.
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Uncertainty-Aware Offline Data-Driven Multi-Objective Optimization
A dual-ranking strategy improves offline data-driven multi-objective optimization by prioritizing solutions that score well on both predicted performance and low uncertainty across different surrogate models.
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On the Uncertainty Quantification Ability of Tabular Foundation Models
Empirical study finds GPs often superior to TabPFN for UQ and accuracy in data-scarce tabular regression, with TabPFN competitive in complex high-dimensional high-data regimes.
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Towards Scalable Gaussian Process Modeling
Implements ASMC in GEBHM to scale GP hyperparameter estimation to large datasets, saving time while maintaining predictability.