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 251–300 of 1963 papers

TitleStatusHype
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization—0
Bayesian Kernelized Tensor Factorization as Surrogate for Bayesian Optimization—0
Bayesian Kernel Shaping for Learning Control—0
Analytical results for uncertainty propagation through trained machine learning regression models—0
Band-Limited Gaussian Processes: The Sinc Kernel—0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Bandits for Learning to Explain from Explanations—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
All You Need is a Good Functional Prior for Bayesian Deep Learning—0
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Aligned Multi-Task Gaussian Process—0
Conditional Neural Processes for Molecules—0
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
Automating the Design of Multi-band Microstrip Antennas via Uniform Cross-Entropy Optimization—0
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Algorithmic Linearly Constrained Gaussian Processes—0
A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes—0
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference—0
25 Tweets to Know You: A New Model to Predict Personality with Social Media—0
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Auto-Differentiating Linear Algebra—0
Learning-based attacks in cyber-physical systems—0
A Hybrid Approach for Trajectory Control Design—0
A universal probabilistic spike count model reveals ongoing modulation of neural variability—0
A Fast and Greedy Subset-of-Data (SoD) Scheme for Sparsification in Gaussian processes—0
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes—0
Aggregating Dependent Gaussian Experts in Local Approximation—0
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty—0
A Unified Theory of Quantum Neural Network Loss Landscapes—0
A Unified Kernel for Neural Network Learning—0
Aggregated Multi-output Gaussian Processes with Knowledge Transfer Across Domains—0
Compositionally-Warped Gaussian Processes—0
Computational Graph Completion—0
AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites—0
A Generalized Unified Skew-Normal Process with Neural Bayes Inference—0
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
A generalised form for a homogeneous population of structures using an overlapping mixture of Gaussian processes—0
STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes—0
A Tucker decomposition process for probabilistic modeling of diffusion magnetic resonance imaging—0
A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes—0
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning—0
Attentive Gaussian processes for probabilistic time-series generation—0
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Benchmark Results

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