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 176–200 of 1963 papers

TitleStatusHype
Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processes—0
Predicting Electricity Consumption with Random Walks on Gaussian Processes—0
Inference for Large Scale Regression Models with Dependent Errors—0
Operator Learning with Gaussian ProcessesCode1
Heartbeat classification using various machine learning models: A comparative studyCode0
Multi-Task Combinatorial Bandits for Budget Allocation—0
Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes—0
Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentialsCode0
Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Gaussian Processes with Noisy Regression Inputs for Dynamical Systems—0
Incremental Structure Discovery of Classification via Sequential Monte Carlo—0
Adaptive Basis Function Selection for Computationally Efficient PredictionsCode0
Posterior Covariance Structures in Gaussian Processes—0
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?Code0
Artificial Neural Network and Deep Learning: Fundamentals and Theory—0
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations—0
Model-Based Transfer Learning for Contextual Reinforcement LearningCode1
Simultaneous and Meshfree Topology Optimization with Physics-informed Gaussian ProcessesCode0
Dirichlet Logistic Gaussian Processes for Evaluation of Black-Box Stochastic Systems under Complex Requirements—0
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks—0
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection—0
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational InferenceCode0
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Benchmark Results

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