SOTAVerified

Gaussian Processes

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.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 276–300 of 1963 papers

TitleStatusHype
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy SearchCode0
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detectionCode0
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression—0
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility—0
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction—0
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Function-space Parameterization of Neural Networks for Sequential LearningCode0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
Learning High-Order Control Barrier Functions for Safety-Critical Control with Gaussian Processes—0
Is Data All That Matters? The Role of Control Frequency for Learning-Based Sampled-Data Control of Uncertain SystemsCode0
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes—0
Mechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints—0
Chronos: Learning the Language of Time SeriesCode7
PMBO: Enhancing Black-Box Optimization through Multivariate Polynomial Surrogates—0
Explainable Learning with Gaussian ProcessesCode0
Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization—0
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration—0
Robustness bounds on the successful adversarial examples in probabilistic models: Implications from Gaussian processes—0
Deep Horseshoe Gaussian Processes—0
Fusion of Gaussian Processes Predictions with Monte Carlo Sampling—0
Mixed Strategy Nash Equilibrium for Crowd Navigation—0
Sketching the Heat Kernel: Using Gaussian Processes to Embed Data—0
Multi-Fidelity Residual Neural Processes for Scalable Surrogate ModelingCode1
Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models—0
Efficiently Computable Safety Bounds for Gaussian Processes in Active LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified