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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1100 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
Semi-described and semi-supervised learning with Gaussian processes—0
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes—0
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze—0
Semi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors—0
Sentiment analysis with genetically evolved Gaussian kernels—0
Sequence Alignment with Dirichlet Process Mixtures—0
Sequential Estimation of Gaussian Process-based Deep State-Space Models—0
Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization—0
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes—0
Sharp Calibrated Gaussian Processes—0
SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation—0
Ensembling methods for countrywide short term forecasting of gas demand—0
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes—0
Short-term Volatility Estimation for High Frequency Trades using Gaussian processes (GPs)—0
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data—0
Signal-based Bayesian Seismic Monitoring—0
Simultaneous Twin Kernel Learning using Polynomial Transformations for Structured Prediction—0
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces—0
Sketching the Heat Kernel: Using Gaussian Processes to Embed Data—0
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting—0
Small Sample Spaces for Gaussian Processes—0
Smart Forgetting for Safe Online Learning with Gaussian Processes—0
Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods—0
Sparse Convolved Gaussian Processes for Multi-output Regression—0
Sparse Covariance Modeling in High Dimensions with Gaussian Processes—0
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Benchmark Results

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