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 1–10 of 1963 papers

TitleStatusHype
Fast Gaussian Processes under Monotonicity Constraints—0
MathOptAI.jl: Embed trained machine learning predictors into JuMP modelsCode2
Scalable Machine Learning Algorithms using Path Signatures—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes—0
Statistical Machine Learning for Astronomy -- A TextbookCode2
Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated DataCode0
The Currents of Conflict: Decomposing Conflict Trends with Gaussian ProcessesCode0
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data—0
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Benchmark Results

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