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 1151–1175 of 1963 papers

TitleStatusHype
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
String and Membrane Gaussian Processes—0
String Gaussian Process Kernels—0
Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression—0
Structure-Aware Random Fourier Kernel for Graphs—0
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective—0
Structured Machine Learning Tools for Modelling Characteristics of Guided Waves—0
Structured Variational Inference for Coupled Gaussian Processes—0
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators—0
Student-t Processes as Alternatives to Gaussian Processes—0
Student-t processes as infinite-width limits of posterior Bayesian neural networks—0
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children—0
Sum-of-Squares Program and Safe Learning On Maximizing the Region of Attraction of Partially Unknown Systems—0
Support Collapse of Deep Gaussian Processes with Polynomial Kernels for a Wide Regime of Hyperparameters—0
Demystifying Spectral Bias on Real-World Data—0
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes—0
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions—0
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points—0
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)—0
Temporal alignment and latent Gaussian process factor inference in population spike trains—0
Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding—0
Temporal Knowledge Graph Embedding based on Multivariate Gaussian Process—0
Tensor Regression Meets Gaussian Processes—0
The Automatic Statistician: A Relational Perspective—0
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Benchmark Results

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