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 1301–1350 of 1963 papers

TitleStatusHype
Disentangling Trainability and Generalization in Deep Neural Networks—0
Randomly Projected Additive Gaussian Processes for RegressionCode0
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase—0
Quantile Propagation for Wasserstein-Approximate Gaussian ProcessesCode0
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
On the relationship between multitask neural networks and multitask Gaussian Processes—0
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax—0
lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal dataCode0
Warped Input Gaussian Processes for Time Series ForecastingCode0
Scalable Bayesian Preference Learning for CrowdsCode0
Numerical Gaussian process Kalman filtering—0
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian ProcessesCode1
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes—0
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes—0
Safety Guarantees for Planning Based on Iterative Gaussian Processes—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
Machine Learning for a Low-cost Air Pollution Network—0
Learning of Weighted Multi-layer Networks via Dynamic Social Spaces, with Application to Financial Interbank TransactionsCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process ModelCode0
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections—0
Scalable Exact Inference in Multi-Output Gaussian ProcessesCode1
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness—0
Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees—0
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models—0
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO—0
Statistical Model Aggregation via Parameter MatchingCode0
Modelling Uncertainty in Collaborative Document Quality Assessment—0
Continual Multi-task Gaussian ProcessesCode0
Recovering BanditsCode0
Function-Space Distributions over KernelsCode0
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation—0
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian ProcessesCode1
Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei—0
Implicit Posterior Variational Inference for Deep Gaussian ProcessesCode0
Sparse Orthogonal Variational Inference for Gaussian ProcessesCode1
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
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Deep Structured Mixtures of Gaussian ProcessesCode0
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Benchmark Results

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