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 951–975 of 1963 papers

TitleStatusHype
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
One-parameter family of acquisition functions for efficient global optimization—0
Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Safe Chance Constrained Reinforcement Learning for Batch Process ControlCode0
Correlated Dynamics in Marketing Sensitivities—0
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional StructureCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Mixtures of Gaussian Processes for regression under multiple prior distributions—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion—0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
GPflux: A Library for Deep Gaussian ProcessesCode1
Uncertainty-aware Remaining Useful Life predictor—0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes—0
Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Simultaneous Reconstruction and Uncertainty Quantification for Tomography—0
Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes—0
Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization—0
Gaussian Process Convolutional Dictionary Learning—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Solving and Learning Nonlinear PDEs with Gaussian ProcessesCode1
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Benchmark Results

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