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 101–150 of 1963 papers

TitleStatusHype
Active Testing: Sample-Efficient Model EvaluationCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Actually Sparse Variational Gaussian ProcessesCode1
Constrained Causal Bayesian OptimizationCode1
MOGPTK: The Multi-Output Gaussian Process ToolkitCode1
Multi-class Gaussian Process Classification with Noisy InputsCode1
Accounting for Input Noise in Gaussian Process Parameter RetrievalCode1
Convergence of Sparse Variational Inference in Gaussian Processes RegressionCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Multi-task Causal Learning with Gaussian ProcessesCode1
70 years of machine learning in geoscience in reviewCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Neural Networks and Quantum Field TheoryCode1
Neural Tangent Kernel: Convergence and Generalization in Neural NetworksCode1
NUBO: A Transparent Python Package for Bayesian OptimizationCode1
Operator Learning with Gaussian ProcessesCode1
PACOH: Bayes-Optimal Meta-Learning with PAC-GuaranteesCode1
Pathwise Conditioning of Gaussian ProcessesCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Positional Encoder Graph Neural Networks for Geographic DataCode1
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
Probabilistic Estimation of Instantaneous Frequencies of Chirp SignalsCode1
Deep Gaussian Process Emulation using Stochastic ImputationCode1
Probabilistic selection of inducing points in sparse Gaussian processesCode1
Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsCode1
Random Forests for dependent dataCode1
Deep Random Features for Scalable Interpolation of Spatiotemporal DataCode1
Deep State-Space Gaussian ProcessesCode1
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
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
A tutorial on learning from preferences and choices with Gaussian ProcessesCode1
Disentangling Derivatives, Uncertainty and Error in Gaussian Process ModelsCode1
An Intuitive Tutorial to Gaussian Process RegressionCode1
AutoIP: A United Framework to Integrate Physics into Gaussian ProcessesCode1
Efficiently Sampling Functions from Gaussian Process PosteriorsCode1
A Unifying Variational Framework for Gaussian Process Motion PlanningCode1
Time series forecasting with Gaussian Processes needs priorsCode1
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
Pre-trained Gaussian Processes for Bayesian OptimizationCode1
Deep Pipeline Embeddings for AutoMLCode1
Bayesian Deep Ensembles via the Neural Tangent KernelCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Gaussian Processes for Missing Value ImputationCode1
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian ProcessesCode1
Dense Gaussian Processes for Few-Shot SegmentationCode1
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Benchmark Results

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