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 51–75 of 1963 papers

TitleStatusHype
Constrained Causal Bayesian OptimizationCode1
Graph Neural Processes for Spatio-Temporal ExtrapolationCode1
Deep Pipeline Embeddings for AutoMLCode1
Physics Inspired Approaches To Understanding Gaussian ProcessesCode1
NUBO: A Transparent Python Package for Bayesian OptimizationCode1
Disentangled Multi-Fidelity Deep Bayesian Active LearningCode1
Optimizing Hyperparameters with Conformal Quantile RegressionCode1
Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear PDEsCode1
Actually Sparse Variational Gaussian ProcessesCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modellingCode1
Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black HolesCode1
Traffic State Estimation from Vehicle Trajectories with Anisotropic Gaussian ProcessesCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Neural-BO: A Black-box Optimization Algorithm using Deep Neural NetworksCode1
Gaussian processes at the Helm(holtz): A more fluid model for ocean currentsCode1
Guided Deep Kernel LearningCode1
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised LearningCode1
Towards Practical Preferential Bayesian Optimization with Skew Gaussian ProcessesCode1
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spacesCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Learning safety in model-based Reinforcement Learning using MPC and Gaussian ProcessesCode1
Fast and robust Bayesian Inference using Gaussian Processes with GPryCode1
Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical ReactorsCode1
Understanding of the properties of neural network approaches for transient light curve approximationsCode1
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Benchmark Results

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