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 201–250 of 1963 papers

TitleStatusHype
Gaussian Processes for Data-Efficient Learning in Robotics and ControlCode0
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian ProcessesCode0
Gaussian Process Behaviour in Wide Deep Neural NetworksCode0
Function-space Parameterization of Neural Networks for Sequential LearningCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Fully Bayesian inference for latent variable Gaussian process modelsCode0
Functional Variational Bayesian Neural NetworksCode0
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial dataCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Flexible and efficient emulation of spatial extremes processes via variational autoencodersCode0
Function-Space Distributions over KernelsCode0
Gaussian Processes for Monitoring Air-Quality in KampalaCode0
Federated Causal Inference from Observational DataCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Explainable Learning with Gaussian ProcessesCode0
Fast Adaptation with Linearized Neural NetworksCode0
Evolving-Graph Gaussian ProcessesCode0
Batch Bayesian Optimization via Local PenalizationCode0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Exact Gaussian Processes on a Million Data PointsCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Adaptive Cholesky Gaussian ProcessesCode0
Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-ConceptCode0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
All your loss are belong to BayesCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Bayesian Causal Inference with Gaussian Process NetworksCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Amortized Variational Inference: When and Why?Code0
Adaptive Basis Function Selection for Computationally Efficient PredictionsCode0
Entropic Trace Estimates for Log DeterminantsCode0
Bayesian Deep Learning on a Quantum ComputerCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Avoiding pathologies in very deep networksCode0
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite NetworksCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
The Debiased Spatial Whittle LikelihoodCode0
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Benchmark Results

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