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Stochastic Processes

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Stochastic Processes

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Research Frontiers in Gaussian Process Regression

Nonparametric Bayesian approach using stochastic processes as priors for machine learning and prediction tasks.

Sparse Kernel Approximations in High-Dimensional Inference
Non-Stationary Covariance Structures and Adaptive Learning
Scalable Gaussian Processes via Inducing Variable Methods
Uncertainty Quantification in Deep Kernel Learning
Spectral Methods for Infinite-Dimensional GP Representations
Multi-Task and Transfer Learning via GP Coupling
Functional Data Analysis Through Gaussian Process Decomposition
Causal Inference and Counterfactuals in GP Frameworks
Heteroscedastic and Non-Gaussian Likelihoods in GP Models
Geometric Perspectives on Kernel Manifolds and GP Geometry

All Stochastic Processes PhD categories