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 1051–1075 of 1963 papers

TitleStatusHype
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition—0
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization—0
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees—0
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase—0
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation—0
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction—0
Scalable Levy Process Priors for Spectral Kernel Learning—0
Scalable Machine Learning Algorithms using Path Signatures—0
Scalable Meta-Learning with Gaussian Processes—0
Scalable Model-Based Gaussian Process Clustering—0
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation—0
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference—0
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization—0
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes—0
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions—0
Scalable Uncertainty for Computer Vision with Functional Variational Inference—0
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO—0
Scale invariant process regression: Towards Bayesian ML with minimal assumptions—0
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure—0
Scaling Gaussian Processes with Derivative Information Using Variational Inference—0
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation—0
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing—0
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes—0
Evaluating feasibility of batteries for second-life applications using machine learning—0
Self-Correcting Bayesian Optimization through Bayesian Active Learning—0
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Benchmark Results

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