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 151–175 of 1963 papers

TitleStatusHype
Active Bayesian Causal InferenceCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Exploring the Impact of Noise on Hybrid Inversion of PROSAIL RTM on Sentinel-2 DataCode1
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Gaussian Processes for Missing Value ImputationCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processesCode1
GPflux: A Library for Deep Gaussian ProcessesCode1
GP-GS: Gaussian Processes for Enhanced Gaussian SplattingCode1
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised LearningCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
Guided Deep Kernel LearningCode1
Healing Products of Gaussian ProcessesCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Variational multiple shooting for Bayesian ODEs with Gaussian processesCode1
Implicit Gaussian process representation of vector fields over arbitrary latent manifoldsCode1
On Feature Collapse and Deep Kernel Learning for Single Forward Pass UncertaintyCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Kalman meets Bellman: Improving Policy Evaluation through Value TrackingCode1
Deep Pipeline Embeddings for AutoMLCode1
Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciencesCode1
GP+: A Python Library for Kernel-based learning via Gaussian ProcessesCode1
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Benchmark Results

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