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 1451–1500 of 1963 papers

TitleStatusHype
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic FeaturesCode0
Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective—0
Novel Uncertainty Framework for Deep Learning Ensembles—0
Robust Deep Gaussian Processes—0
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian OptimizationCode0
Generalized Variational Inference: Three arguments for deriving new PosteriorsCode0
Sentiment analysis with genetically evolved Gaussian kernels—0
A Gaussian process latent force model for joint input-state estimation in linear structural systems—0
Deep Random Splines for Point Process Intensity Estimation—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Exact Gaussian Processes on a Million Data PointsCode0
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence—0
Deep Gaussian Processes for Multi-fidelity ModelingCode0
Pairwise Comparisons with Flexible Time-DynamicsCode0
Functional Variational Bayesian Neural NetworksCode0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Learning Gaussian Policies from Corrective Human Feedback—0
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test EvaluationsCode0
Deep Random Splines for Point Process Intensity Estimation of Neural Population DataCode0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data—0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process ApproachCode0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
Bayesian Anomaly Detection and Classification—0
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent—0
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation—0
Low-pass filtering as Bayesian inference—0
The role of a layer in deep neural networks: a Gaussian Process perspective—0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach—0
Ensembling methods for countrywide short term forecasting of gas demand—0
Towards Practical Lipschitz Bandits—0
On the Limitations of Representing Functions on Sets—0
Meta-Learning Mean Functions for Gaussian Processes—0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC—0
Gaussian processes with linear operator inequality constraintsCode0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Performance prediction of data streams on high-performance architecture—0
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler—0
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Benchmark Results

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