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 551–575 of 1963 papers

TitleStatusHype
Sequential Estimation of Gaussian Process-based Deep State-Space Models—0
Intrinsic Bayesian Optimisation on Complex Constrained Domain—0
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and PredictionCode0
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics—0
On the role of Model Uncertainties in Bayesian Optimization—0
Modeling the evolution of temporal knowledge graphs with uncertainty—0
Machine learning methods for prediction of breakthrough curves in reactive porous media—0
Robust Bayesian Target Value Optimization—0
Application of machine learning to gas flaring—0
Quantile Autoregression-based Non-causality Testing—0
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising—0
Robust and Scalable Gaussian Process Regression and Its ApplicationsCode0
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential EquationsCode0
Extrinsic Bayesian Optimizations on Manifolds—0
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processesCode0
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact SupervisionCode0
Generative structured normalizing flow Gaussian processes applied to spectroscopic data—0
Information-Theoretic Safe Exploration with Gaussian ProcessesCode0
GAUCHE: A Library for Gaussian Processes in ChemistryCode2
Deep Kernel Learning for Mortality Prediction in the Face of Temporal ShiftCode0
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Benchmark Results

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