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 1251–1300 of 1963 papers

TitleStatusHype
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes—0
Linear-time inference for Gaussian Processes on one dimension—0
Amortized variance reduction for doubly stochastic objectives—0
Modelling Human Active Search in Optimizing Black-box Functions—0
Scalable Uncertainty for Computer Vision with Functional Variational Inference—0
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations—0
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with DerivativesCode0
Knot Selection in Sparse Gaussian Processes with a Variational Objective FunctionCode0
Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns—0
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change ScenariosCode1
A Framework for Interdomain and Multioutput Gaussian ProcessesCode2
Stable behaviour of infinitely wide deep neural networksCode0
Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods—0
Mixed Strategies for Robust Optimization of Unknown Objectives—0
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes—0
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process ModelsCode0
Near-linear Time Gaussian Process Optimization with Adaptive Batching and ResparsificationCode1
Knot Selection in Sparse Gaussian Processes—0
Efficiently Sampling Functions from Gaussian Process PosteriorsCode1
Deep Sigma Point Processes—0
Bayesian Deep Learning and a Probabilistic Perspective of GeneralizationCode1
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite NetworksCode0
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes—0
Online Parameter Estimation for Safety-Critical Systems with Gaussian Processes—0
Kalman meets Bellman: Improving Policy Evaluation through Value TrackingCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
Combining Parametric Land Surface Models with Machine Learning—0
PACOH: Bayes-Optimal Meta-Learning with PAC-GuaranteesCode1
Graph Convolutional Gaussian Processes For Link Prediction—0
MOGPTK: The Multi-Output Gaussian Process ToolkitCode1
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural NetworksCode0
Conditional Deep Gaussian Processes: multi-fidelity kernel learningCode0
Linearly Constrained Neural NetworksCode0
Linearly Constrained Gaussian Processes with Boundary Conditions—0
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes—0
Estimation of Z-Thickness and XY-Anisotropy of Electron Microscopy Images using Gaussian ProcessesCode0
Transport Gaussian Processes for Regression—0
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness—0
Multi-class Gaussian Process Classification with Noisy InputsCode1
Estimating Latent Demand of Shared Mobility through Censored Gaussian ProcessesCode0
Scalable Hyperparameter Optimization with Lazy Gaussian ProcessesCode0
Quantified limits of the nuclear landscape—0
Doubly Sparse Variational Gaussian Processes—0
Considering discrepancy when calibrating a mechanistic electrophysiology modelCode0
Bayesian Quantile and Expectile Optimisation—0
Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational AutoencodersCode1
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes—0
Inter-domain Deep Gaussian Processes with RKHS Fourier Features—0
Healing Gaussian Process Experts—0
Influenza Forecasting Framework based on Gaussian Processes—0
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Benchmark Results

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