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 676–700 of 1963 papers

TitleStatusHype
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
Graph Based Gaussian Processes on Restricted Domains—0
Bayesian Layers: A Module for Neural Network Uncertainty—0
Differentiating the multipoint Expected Improvement for optimal batch design—0
Bayesian Kernel Shaping for Learning Control—0
Analytical results for uncertainty propagation through trained machine learning regression models—0
Differentially Private Regression and Classification with Sparse Gaussian Processes—0
Differentially Private Gaussian Processes—0
Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization—0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Analytical Results for the Error in Filtering of Gaussian Processes—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?—0
Bayesian Inference of Log Determinants—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Design of Experiments for Verifying Biomolecular Networks—0
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty—0
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design—0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Analysis of Financial Credit Risk Using Machine Learning—0
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Benchmark Results

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