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 651–700 of 1963 papers

TitleStatusHype
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial LibrariesCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
Cluster-Specific Predictions with Multi-Task Gaussian ProcessesCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
Direct loss minimization algorithms for sparse Gaussian processesCode0
Adaptive RKHS Fourier Features for Compositional Gaussian Process ModelsCode0
Functional Variational Bayesian Neural NetworksCode0
Multi-task Learning for Aggregated Data using Gaussian ProcessesCode0
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor DataCode0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
Function-Space Distributions over KernelsCode0
Neural Non-Stationary Spectral KernelCode0
Gaussian processes with linear operator inequality constraintsCode0
Neural signature kernels as infinite-width-depth-limits of controlled ResNetsCode0
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU AccelerationCode0
Non-Euclidean Universal ApproximationCode0
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian ProcessesCode0
Nonmyopic Global Optimisation via Approximate Dynamic ProgrammingCode0
Model Criticism in Latent SpaceCode0
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian ProcessesCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Federated Causal Inference from Observational DataCode0
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian ProcessesCode0
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