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 1351–1375 of 1963 papers

TitleStatusHype
Recovering BanditsCode0
Function-Space Distributions over KernelsCode0
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation—0
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei—0
Implicit Posterior Variational Inference for Deep Gaussian ProcessesCode0
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric UncertaintyCode0
Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processes—0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Deep Kernels with Probabilistic Embeddings for Small-Data LearningCode0
Nonstationary Multivariate Gaussian Processes for Electronic Health Records—0
Regularized Sparse Gaussian Processes—0
On the expected behaviour of noise regularised deep neural networks as Gaussian processes—0
Deep Structured Mixtures of Gaussian ProcessesCode0
Partial Separability and Functional Graphical Models for Multivariate Gaussian ProcessesCode0
A Learnable Safety MeasureCode0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
Probabilistic Deep Ordinal Regression Based on Gaussian Processes—0
Non-Gaussian processes and neural networks at finite widths—0
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory—0
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes—0
Disentangling Trainability and Generalization in Deep Learning—0
Localizing and Amortizing: Efficient Inference for Gaussian Processes—0
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Benchmark Results

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