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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–350 of 1963 papers

TitleStatusHype
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Attentive Gaussian processes for probabilistic time-series generation—0
A General Framework for Fair Regression—0
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes—0
Bayesian Deconditional Kernel Mean Embeddings—0
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures—0
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel—0
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots—0
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation—0
A Gaussian Process Regression Model for Distribution Inputs—0
A temporal model of text periodicities using Gaussian Processes—0
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression—0
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking—0
Active learning of neural response functions with Gaussian processes—0
Asynchronous Distributed Variational Gaussian Processes for Regression—0
Asymmetric kernel in Gaussian Processes for learning target variance—0
A Gaussian Process perspective on Convolutional Neural Networks—0
Correlated Dynamics in Marketing Sensitivities—0
Optimal Privacy-Aware Stochastic Sampling—0
Accelerating ABC methods using Gaussian processes—0
Convergence Rates of Constrained Expected Improvement—0
A Statistical Machine Learning Approach to Yield Curve Forecasting—0
A Gaussian Process Model for Ordinal Data with Applications to Chemoinformatics—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Associative embeddings for large-scale knowledge transfer with self-assessment—0
Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression—0
A Gaussian process latent force model for joint input-state estimation in linear structural systems—0
Data Association with Gaussian Processes—0
Assessing Quality Estimation Models for Sentence-Level Prediction—0
A spectrum of physics-informed Gaussian processes for regression in engineering—0
Active Learning for Regression with Aggregated Outputs—0
A Sparse Gaussian Process Framework for Photometric Redshift Estimation—0
A Sparse Expansion For Deep Gaussian Processes—0
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options—0
A Bulirsch-Stoer algorithm using Gaussian processes—0
Data-Driven Approaches for Modelling Target Behaviour—0
Data-driven Bayesian Control of Port-Hamiltonian Systems—0
Current Methods for Drug Property Prediction in the Real World—0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Compositionally-Warped Gaussian Processes—0
Composite likelihood estimation of stationary Gaussian processes with a view toward stochastic volatility—0
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs—0
Composite Gaussian Processes: Scalable Computation and Performance Analysis—0
Composite Gaussian Processes Flows for Learning Discontinuous Multimodal Policies—0
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction—0
Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verification—0
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference—0
Artificial Neural Network and Deep Learning: Fundamentals and Theory—0
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Benchmark Results

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