Gaussian Processes
568 papers with code • 1 benchmarks • 5 datasets
Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.
Benchmarks
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Libraries
Use these libraries to find Gaussian Processes models and implementations
UCI Machine Learning Repository
MPI FAUST Dataset
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Latest papers with no code
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
By utilizing a technique called Kernel Packets (KP), we prove that the convergence rate of Back-fitting is no faster than $(1-\mathcal{O}(\frac{1}{n}))^t$, where $n$ and $t$ denote the data size and the iteration number, respectively.
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility
We develop a framework for composite likelihood inference of parametric continuous-time stationary Gaussian processes.
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction
Previous work has shown that using such informative priors to regularize probabilistic deep learning (DL) models increases their performance and data-efficiency.
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems.
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
Our model selection criteria allow significantly faster and high quality model selection of Gaussian process models.
Learning High-Order Control Barrier Functions for Safety-Critical Control with Gaussian Processes
However, the effectiveness of CBFs is closely tied to the system model.
Mechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints
It investigates how to efficiently integrate Computer-Aided Design (CAD) simulations with Bayesian Optimization (BO) and a constrained design space, aiming to enhance the design optimization process beyond the confines of traditional kinematic and dynamic analysis.
PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates
We introduce a surrogate-based black-box optimization method, termed Polynomial-model-based optimization (PMBO).
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
We address this issue by proposing ShapleyBO, a framework for interpreting BO's proposals by game-theoretic Shapley values. They quantify each parameter's contribution to BO's acquisition function.
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization
In this letter, we propose a framework for adapting the controller's parameters based on learning optimal solutions from contextual black-box optimization problems.
