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
Recurrent Memory for Online Interdomain Gaussian Processes—0
New Bounds for Sparse Variational Gaussian Processes—0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
Koopman-Equivariant Gaussian Processes—0
Bayesian Optimization by Kernel Regression and Density-based Exploration—0
Distributionally Robust Model Predictive Control with Mixture of Gaussian Processes—0
Decentralized Online Ensembles of Gaussian Processes for Multi-Agent SystemsCode0
Tighter sparse variational Gaussian processes—0
Student-t processes as infinite-width limits of posterior Bayesian neural networks—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
Composite Gaussian Processes Flows for Learning Discontinuous Multimodal Policies—0
Robust and Conjugate Spatio-Temporal Gaussian ProcessesCode0
Gaussian processes for dynamics learning in model predictive control—0
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers—0
Learning Hyperparameters via a Data-Emphasized Variational ObjectiveCode0
The Price of Linear Time: Error Analysis of Structured Kernel Interpolation—0
PDE-DKL: PDE-constrained deep kernel learning in high dimensionalityCode0
Machine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers—0
Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural NetworksCode0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles—0
Diffusion-aware Censored Gaussian Processes for Demand ModellingCode0
Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging DataCode0
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial dataCode0
Issues with Neural Tangent Kernel Approach to Neural NetworksCode0
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Benchmark Results

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