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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 1251–1275 of 1963 papers

TitleStatusHype
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization—0
Grouped Gaussian Processes for Solar Power Prediction—0
Group Importance Sampling for Particle Filtering and MCMC—0
Guided Bayesian Optimization: Data-Efficient Controller Tuning with Digital Twin—0
Hands-on Experience with Gaussian Processes (GPs): Implementing GPs in Python - I—0
Harmonizable mixture kernels with variational Fourier features—0
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling—0
Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes—0
Healing Gaussian Process Experts—0
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry—0
Heavy-Tailed Process Priors for Selective Shrinkage—0
Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data—0
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution—0
Hierarchical Gaussian Processes with Wasserstein-2 Kernels—0
Hierarchical Non-Stationary Temporal Gaussian Processes With L^1-Regularization—0
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery—0
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
How to turn your camera into a perfect pinhole model—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes—0
Identifying Causal Direction via Variational Bayesian Compression—0
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Benchmark Results

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