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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

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Showing 501–525 of 1963 papers

TitleStatusHype
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Raven's Progressive Matrices Completion with Latent Gaussian Process PriorsCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Benefits of Monotonicity in Safe Exploration with Gaussian ProcessesCode0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
Recursive Estimation for Sparse Gaussian Process RegressionCode0
A conditional one-output likelihood formulation for multitask Gaussian processesCode0
Diffusion-aware Censored Gaussian Processes for Demand ModellingCode0
Residual Deep Gaussian Processes on ManifoldsCode0
Revisiting Active Sets for Gaussian Process DecodersCode0
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian ProcessesCode0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
Gaussian Processes for Data-Efficient Learning in Robotics and ControlCode0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Fast covariance parameter estimation of spatial Gaussian process models using neural networksCode0
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional DataCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Bayesian Structured Prediction Using Gaussian ProcessesCode0
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian ProcessesCode0
Bayesian Semi-supervised Learning with Graph Gaussian ProcessesCode0
Fast Approximate Multi-output Gaussian ProcessesCode0
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Benchmark Results

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