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 1426–1450 of 1963 papers

TitleStatusHype
Machine learning methods for prediction of breakthrough curves in reactive porous media—0
Mapping Input Noise to Escape Noise in Integrate-and-fire neurons: A Level-Crossing Approach—0
Mapping Leaf Area Index with a Smartphone and Gaussian Processes—0
Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flows—0
Markov Chain Monte Carlo with Gaussian Process Emulation for a 1D Hemodynamics Model of CTEPH—0
Learning Time-Varying Multi-Region Communications via Scalable Markovian Gaussian Processes—0
Martingale Posterior Neural Processes—0
Matching models across abstraction levels with Gaussian Processes—0
Matérn Gaussian Processes on Graphs—0
Contraction L_1-Adaptive Control using Gaussian Processes—0
MCMC for Variationally Sparse Gaussian Processes—0
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes—0
Measuring the robustness of Gaussian processes to kernel choice—0
Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep—0
Mechanism Design Optimization through CAD-Based Bayesian Optimization and Quantified Constraints—0
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning—0
Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimation—0
Meta Reinforcement Learning with Latent Variable Gaussian Processes—0
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach—0
Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization—0
Probability-Generating Function Kernels for Spherical Data—0
Mixed-Stationary Gaussian Process for Flexible Non-Stationary Modeling of Spatial Outcomes—0
Mixed Strategies for Robust Optimization of Unknown Objectives—0
Mixed Strategy Nash Equilibrium for Crowd Navigation—0
Mixtures of Gaussian Processes for regression under multiple prior distributions—0
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Benchmark Results

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