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 126–150 of 1963 papers

TitleStatusHype
Deep Kernel LearningCode1
Deep Pipeline Embeddings for AutoMLCode1
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
Batched Energy-Entropy acquisition for Bayesian OptimizationCode1
A Rate-Distortion View of Uncertainty QuantificationCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Dense Gaussian Processes for Few-Shot SegmentationCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Disentangled Multi-Fidelity Deep Bayesian Active LearningCode1
An Intuitive Tutorial to Gaussian Process RegressionCode1
AutoIP: A United Framework to Integrate Physics into Gaussian ProcessesCode1
Supervising the Multi-Fidelity Race of Hyperparameter ConfigurationsCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Exact, Fast and Expressive Poisson Point Processes via Squared Neural FamiliesCode1
Exploration in Online Advertising Systems with Deep Uncertainty-Aware LearningCode1
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Deep State-Space Gaussian ProcessesCode1
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Show:102550
← PrevPage 6 of 79Next →

Benchmark Results

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