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 681–690 of 1963 papers

TitleStatusHype
Learning Choice Functions with Gaussian ProcessesCode0
Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions—0
A Fully-Automated Framework Integrating Gaussian Process Regression and Bayesian Optimization to Design Pin-Fins—0
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimizationCode0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
Sequential Estimation of Gaussian Process-based Deep State-Space Models—0
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and PredictionCode0
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Benchmark Results

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