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 501–550 of 1963 papers

TitleStatusHype
Gaussian Process Priors for Boundary Value Problems of Linear Partial Differential EquationsCode0
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processesCode0
Beyond Intuition, a Framework for Applying GPs to Real-World DataCode0
Gaussian Process Kernels for Pattern Discovery and ExtrapolationCode0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Gaussian Process Random FieldsCode0
Scalable Generalized Dynamic Topic ModelsCode0
Diffusion-aware Censored Gaussian Processes for Demand ModellingCode0
Scalable Hyperparameter Optimization with Products of Gaussian Process ExpertsCode0
Scalable Hyperparameter Optimization with Lazy Gaussian ProcessesCode0
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
A conditional one-output likelihood formulation for multitask Gaussian processesCode0
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
Gaussian processes with linear operator inequality constraintsCode0
Gaussian Process-Gated Hierarchical Mixtures of ExpertsCode0
Gaussian Process Regression NetworksCode0
Gaussian Processes for Monitoring Air-Quality in KampalaCode0
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional DataCode0
Gaussian Processes for Data-Efficient Learning in Robotics and ControlCode0
Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-ConceptCode0
Bayesian Structured Prediction Using Gaussian ProcessesCode0
Gaussian Process Behaviour in Wide Deep Neural NetworksCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Function-space Parameterization of Neural Networks for Sequential LearningCode0
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia LanguageCode0
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Additive Gaussian Processes RevisitedCode0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
Functional Variational Bayesian Neural NetworksCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Federated Causal Inference from Observational DataCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
A Bayesian Perspective of Statistical Machine Learning for Big DataCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Function-Space Distributions over KernelsCode0
Domain Invariant Learning for Gaussian Processes and Bayesian ExplorationCode0
Do ideas have shape? Idea registration as the continuous limit of artificial neural networksCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Doubly Stochastic Variational Inference for Deep Gaussian ProcessesCode0
Additive Gaussian ProcessesCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
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Benchmark Results

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