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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 901–950 of 1963 papers

TitleStatusHype
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning—0
Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes—0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Bayesian Quantile and Expectile Optimisation—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Improving Random Forests by Smoothing—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes—0
Deep Gaussian Processes for Regression using Approximate Expectation Propagation—0
Incremental Ensemble Gaussian Processes—0
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections—0
Incremental Structure Discovery of Classification via Sequential Monte Carlo—0
Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Inducing Gaussian Process Networks—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds—0
A Novel Gaussian Min-Max Theorem and its Applications—0
Inference for Large Scale Regression Models with Dependent Errors—0
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing—0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Dynamic Term Structure Models with Nonlinearities using Gaussian Processes—0
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers—0
Inferring power system dynamics from synchrophasor data using Gaussian processes—0
Deep kernel processes—0
A physics-informed Bayesian optimization method for rapid development of electrical machines—0
Infinite attention: NNGP and NTK for deep attention networks—0
Bayesian Optimization with Tree-structured Dependencies—0
Infinite-Fidelity Coregionalization for Physical Simulation—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes—0
Infinite Mixtures of Multivariate Gaussian Processes—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes—0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Influenza Forecasting Framework based on Gaussian Processes—0
Information Flow Rate for Cross-Correlated Stochastic Processes—0
Information fusion in multi-task Gaussian processes—0
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation—0
Information Theoretic Meta Learning with Gaussian Processes—0
Amortized variance reduction for doubly stochastic objectives—0
Informative Path Planning to Explore and Map Unknown Planetary Surfaces with Gaussian Processes—0
Informative Planning and Online Learning with Sparse Gaussian Processes—0
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction—0
Intrinsic Gaussian Process on Unknown Manifolds with Probabilistic Metrics—0
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Benchmark Results

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