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 1451–1475 of 1963 papers

TitleStatusHype
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic FeaturesCode0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Novel Uncertainty Framework for Deep Learning Ensembles—0
Robust Deep Gaussian Processes—0
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian OptimizationCode0
Generalized Variational Inference: Three arguments for deriving new PosteriorsCode0
Sentiment analysis with genetically evolved Gaussian kernels—0
A Gaussian process latent force model for joint input-state estimation in linear structural systems—0
Deep Random Splines for Point Process Intensity Estimation—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Exact Gaussian Processes on a Million Data PointsCode0
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence—0
Deep Gaussian Processes for Multi-fidelity ModelingCode0
Pairwise Comparisons with Flexible Time-DynamicsCode0
Functional Variational Bayesian Neural NetworksCode0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Learning Gaussian Policies from Corrective Human Feedback—0
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test EvaluationsCode0
Deep Random Splines for Point Process Intensity Estimation of Neural Population DataCode0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data—0
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Benchmark Results

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