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 1026–1050 of 1963 papers

TitleStatusHype
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization—0
Safe Learning of Quadrotor Dynamics Using Barrier Certificates—0
Safe Machine-Learning-supported Model Predictive Force and Motion Control in Robotics—0
Safe model-based design of experiments using Gaussian processes—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes—0
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Framework—0
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes—0
Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian Process Learning—0
Safe Time-Varying Optimization based on Gaussian Processes with Spatio-Temporal Kernel—0
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization—0
Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints (extended version)—0
Safety Guarantees for Planning Based on Iterative Gaussian Processes—0
Safety Verification of Unknown Dynamical Systems via Gaussian Process Regression—0
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates—0
Sample-efficient reinforcement learning using deep Gaussian processes—0
Sample Path Regularity of Gaussian Processes from the Covariance Kernel—0
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks—0
Scalable Bayesian Transformed Gaussian Processes—0
Scalable GAM using sparse variational Gaussian processes—0
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces—0
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors—0
Scalable Gaussian Processes for Supervised Hashing—0
Scalable Gaussian Processes on Discrete Domains—0
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)—0
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Benchmark Results

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