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 1–50 of 1963 papers

TitleStatusHype
Fast Gaussian Processes under Monotonicity Constraints—0
MathOptAI.jl: Embed trained machine learning predictors into JuMP modelsCode2
Scalable Machine Learning Algorithms using Path Signatures—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Statistical Machine Learning for Astronomy -- A TextbookCode2
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes—0
Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated DataCode0
The Currents of Conflict: Decomposing Conflict Trends with Gaussian ProcessesCode0
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data—0
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system—0
Direct Integration of Recursive Gaussian Process Regression Into Extended Kalman Filters With Application to Vapor Compression Cycle Control—0
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs—0
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization—0
Adaptive finite element type decomposition of Gaussian processes—0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Learning-Based Robust Fixed-Time Terminal Sliding Mode Control—0
A Provable Approach for End-to-End Safe Reinforcement Learning—0
STACI: Spatio-Temporal Aleatoric Conformal Inference—0
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition—0
Unsupervised cell segmentation by fast Gaussian ProcessesCode0
Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate dataCode0
Multi-Output Gaussian Processes for Graph-Structured DataCode0
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works—0
Modular Jump Gaussian ProcessesCode0
LLINBO: Trustworthy LLM-in-the-Loop Bayesian OptimizationCode1
Turbocharging Gaussian Process Inference with Approximate Sketch-and-ProjectCode0
Wasserstein Barycenter Gaussian Process based Bayesian Optimization—0
Modèles de Substitution pour les Modèles à base d'Agents : Enjeux, Méthodes et ApplicationsCode0
Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian ProcessesCode0
Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations—0
STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes—0
Convergence Rates of Constrained Expected Improvement—0
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery—0
Fairness-aware Bayes optimal functional classification—0
Probabilistic Wind Power Forecasting via Non-Stationary Gaussian Processes—0
Identifying Causal Direction via Variational Bayesian Compression—0
Improving Random Forests by Smoothing—0
The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning—0
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications—0
Data-driven Approach for Interpolation of Sparse DataCode0
Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes—0
Low-rank computation of the posterior mean in Multi-Output Gaussian Processes—0
TopSpace: spatial topic modeling for unsupervised discovery of multicellular spatial tissue structures in multiplex imaging—0
A Taylor Series Approach to Correction of Input Errors in Gaussian Process Regression—0
Evaluating Uncertainty in Deep Gaussian ProcessesCode0
Gaussian behaviors: representations and data-driven control—0
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep ClassificationCode0
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks—0
Learning-based decentralized control with collision avoidance for multi-agent systems—0
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Benchmark Results

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