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 776–800 of 1963 papers

TitleStatusHype
Rough multifactor volatility for SPX and VIX options—0
Transformers Can Do Bayesian InferenceCode1
GPEX, A Framework For Interpreting Artificial Neural NetworksCode0
Learning-based methods to model small body gravity fields for proximity operations: Safety and Robustness—0
Correlated Product of Experts for Sparse Gaussian Process Regression—0
Learning Rigidity-based Flocking Control with Gaussian Processes—0
Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow—0
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use—0
A Sparse Expansion For Deep Gaussian Processes—0
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
Unified field theoretical approach to deep and recurrent neuronal networks—0
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators—0
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations—0
A Bayesian take on option pricing with Gaussian processes—0
Data Fusion with Latent Map Gaussian Processes—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes—0
Probability-Generating Function Kernels for Spherical Data—0
Structure-Aware Random Fourier Kernel for Graphs—0
A universal probabilistic spike count model reveals ongoing modulation of neural variability—0
Continuous-time edge modelling using non-parametric point processes—0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Dependence between Bayesian neural network units—0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints—0
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Benchmark Results

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