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 51–100 of 1963 papers

TitleStatusHype
Actually Sparse Variational Gaussian ProcessesCode1
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
Guided Deep Kernel LearningCode1
Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational AutoencodersCode1
Building 3D Morphable Models from a Single ScanCode1
High-Dimensional Gaussian Process Inference with DerivativesCode1
Disentangled Multi-Fidelity Deep Bayesian Active LearningCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
On Feature Collapse and Deep Kernel Learning for Single Forward Pass UncertaintyCode1
Causal Discovery via Bayesian OptimizationCode1
Invariance Learning in Deep Neural Networks with Differentiable Laplace ApproximationsCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Dense Gaussian Processes for Few-Shot SegmentationCode1
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningCode1
Deep Gaussian Process Emulation using Stochastic ImputationCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Deep Kernel LearningCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
Deep Pipeline Embeddings for AutoMLCode1
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
Deep State-Space Gaussian ProcessesCode1
Active Bayesian Causal InferenceCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Disentangling Derivatives, Uncertainty and Error in Gaussian Process ModelsCode1
Convolutional conditional neural processes for local climate downscalingCode1
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics IdentificationCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
AutoIP: A United Framework to Integrate Physics into Gaussian ProcessesCode1
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo LabelersCode1
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage GuaranteesCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
Bayesian Optimization of Function NetworksCode1
Bayes-Newton Methods for Approximate Bayesian Inference with PSD GuaranteesCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processesCode1
GP-GS: Gaussian Processes for Enhanced Gaussian SplattingCode1
Constrained Causal Bayesian OptimizationCode1
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Benchmark Results

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