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 901–925 of 1963 papers

TitleStatusHype
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
Dependence between Bayesian neural network units—0
The Fixed-b Limiting Distribution and the ERP of HAR Tests Under Nonstationarity—0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints—0
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes—0
Transfer Learning with Gaussian Processes for Bayesian OptimizationCode0
Accounting for Gaussian Process Imprecision in Bayesian OptimizationCode0
Temporal Knowledge Graph Embedding based on Multivariate Gaussian Process—0
Non-separable Spatio-temporal Graph Kernels via SPDEs—0
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes—0
Optimizing Bayesian acquisition functions in Gaussian Processes—0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes—0
Dual Parameterization of Sparse Variational Gaussian ProcessesCode0
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
Rate of Convergence of Polynomial Networks to Gaussian Processes—0
Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal dataCode0
End-to-End Learning of Deep Kernel Acquisition Functions for Bayesian Optimization—0
Bayesian optimization of distributed neurodynamical controller models for spatial navigation—0
Geometry-Aware Hierarchical Bayesian Learning on Manifolds—0
A comparison of mixed-variables Bayesian optimization approaches—0
Aligned Multi-Task Gaussian Process—0
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels—0
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Benchmark Results

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