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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 601–625 of 1963 papers

TitleStatusHype
Black-box Coreset Variational InferenceCode0
Issues with Neural Tangent Kernel Approach to Neural NetworksCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
JUMBO: Scalable Multi-task Bayesian Optimization using Offline DataCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Bayesian Modeling with Gaussian Processes using the GPstuff ToolboxCode0
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Estimation of Dynamic Gaussian ProcessesCode0
Boundary Exploration for Bayesian Optimization With Unknown Physical ConstraintsCode0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Approximate Latent Force Model InferenceCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
Large-Scale Gaussian Processes via Alternating ProjectionCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical DataCode0
Evolving-Graph Gaussian ProcessesCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Learning Constrained Dynamics with Gauss Principle adhering Gaussian ProcessesCode0
Exact Gaussian Processes on a Million Data PointsCode0
Adversarial Robustness Guarantees for Random Deep Neural NetworksCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
Bayesian Meta-Learning Through Variational Gaussian ProcessesCode0
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Benchmark Results

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