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 951–1000 of 1963 papers

TitleStatusHype
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes—0
One-parameter family of acquisition functions for efficient global optimization—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Safe Chance Constrained Reinforcement Learning for Batch Process ControlCode0
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional StructureCode1
Correlated Dynamics in Marketing Sensitivities—0
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual InformationCode1
Mixtures of Gaussian Processes for regression under multiple prior distributions—0
Convolutional Normalizing Flows for Deep Gaussian Processes—0
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion—0
Distributionally Robust Optimization for Deep Kernel Multiple Instance LearningCode0
GPflux: A Library for Deep Gaussian ProcessesCode1
Uncertainty-aware Remaining Useful Life predictor—0
Adversarial Robustness Guarantees for Gaussian ProcessesCode0
Fast Design Space Exploration of Nonlinear Systems: Part I—0
Safe Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes—0
Prediction of Ultrasonic Guided Wave Propagation in Solid-fluid and their Interface under Uncertainty using Machine Learning—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes—0
Simultaneous Reconstruction and Uncertainty Quantification for Tomography—0
Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization—0
Gaussian Process Convolutional Dictionary Learning—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Solving and Learning Nonlinear PDEs with Gaussian ProcessesCode1
Raven's Progressive Matrices Completion with Latent Gaussian Process PriorsCode0
Data-driven Aerodynamic Analysis of Structures using Gaussian ProcessesCode0
Sparse Algorithms for Markovian Gaussian ProcessesCode0
The Shape of Learning Curves: a ReviewCode0
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey—0
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
Active Testing: Sample-Efficient Model EvaluationCode1
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning—0
Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive ControlCode1
On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach—0
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
Small Sample Spaces for Gaussian Processes—0
ILoSA: Interactive Learning of Stiffness and AttractorsCode1
Fast Adaptation with Linearized Neural Networks—0
Kernel Interpolation for Scalable Online Gaussian ProcessesCode1
Hierarchical Inducing Point Gaussian Process for Inter-domain ObservationsCode0
Similarity measure for sparse time course data based on Gaussian processesCode0
The Promises and Pitfalls of Deep Kernel Learning—0
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference—0
On Feature Collapse and Deep Kernel Learning for Single Forward Pass UncertaintyCode1
Large-width functional asymptotics for deep Gaussian neural networks—0
Output-Weighted Sampling for Multi-Armed Bandits with Extreme PayoffsCode0
Non-asymptotic approximations of neural networks by Gaussian processes—0
Using Distance Correlation for Efficient Bayesian Optimization—0
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Benchmark Results

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