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 1651–1675 of 1963 papers

TitleStatusHype
Subset-of-Data Variational Inference for Deep Gaussian-Processes RegressionCode0
Modulating Scalable Gaussian Processes for Expressive Statistical LearningCode0
Data-Driven Stochastic AC-OPF using Gaussian ProcessesCode0
Mondrian Forests for Large-Scale Regression when Uncertainty MattersCode0
Monotonic Gaussian Process FlowCode0
Quantum-Assisted Hilbert-Space Gaussian Process RegressionCode0
Similarity measure for sparse time course data based on Gaussian processesCode0
Simulation Based Bayesian OptimizationCode0
Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian ProcessesCode0
Random Feature Expansions for Deep Gaussian ProcessesCode0
Morphable Face Models - An Open FrameworkCode0
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimizationCode0
Efficient Deep Gaussian Process Models for Variable-Sized InputCode0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
Randomly Projected Additive Gaussian Processes for RegressionCode0
Gaussian Process Priors for Boundary Value Problems of Linear Partial Differential EquationsCode0
A Learnable Safety MeasureCode0
Gaussian Process Random FieldsCode0
Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian ProcessesCode0
Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian ProcessesCode0
Adaptive Basis Function Selection for Computationally Efficient PredictionsCode0
Gaussian Process Regression NetworksCode0
Dynamic Online Ensembles of Basis ExpansionsCode0
Data-driven Approach for Interpolation of Sparse DataCode0
Multi-fidelity Hierarchical Neural ProcessesCode0
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Benchmark Results

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