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 1401–1450 of 1963 papers

TitleStatusHype
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion—0
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks—0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction—0
Local Gaussian process extrapolation for BART models with applications to causal inference—0
Local Granger Causality—0
Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization—0
Localizing and Amortizing: Efficient Inference for Gaussian Processes—0
Locally adaptive factor processes for multivariate time series—0
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data—0
Locally induced Gaussian processes for large-scale simulation experiments—0
Locally Smoothed Gaussian Process Regression—0
Local Model Feature Transformations—0
Location Dependent Dirichlet Processes—0
Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies—0
Active learning-assisted neutron spectroscopy with log-Gaussian processes—0
Longitudinal Deep Kernel Gaussian Process Regression—0
Low-pass filtering as Bayesian inference—0
Low-rank computation of the posterior mean in Multi-Output Gaussian Processes—0
Machine learning based hyperspectral image analysis: A survey—0
Machine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers—0
Machine Learning for a Low-cost Air Pollution Network—0
Machine Learning for Health: Personalized Models for Forecasting of Alzheimer Disease Progression—0
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system—0
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling—0
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