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 1151–1200 of 1963 papers

TitleStatusHype
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
String and Membrane Gaussian Processes—0
String Gaussian Process Kernels—0
Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression—0
Structure-Aware Random Fourier Kernel for Graphs—0
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective—0
Structured Machine Learning Tools for Modelling Characteristics of Guided Waves—0
Structured Variational Inference for Coupled Gaussian Processes—0
Structure-Preserving Learning Using Gaussian Processes and Variational Integrators—0
Student-t Processes as Alternatives to Gaussian Processes—0
Student-t processes as infinite-width limits of posterior Bayesian neural networks—0
Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children—0
Sum-of-Squares Program and Safe Learning On Maximizing the Region of Attraction of Partially Unknown Systems—0
Support Collapse of Deep Gaussian Processes with Polynomial Kernels for a Wide Regime of Hyperparameters—0
Demystifying Spectral Bias on Real-World Data—0
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes—0
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions—0
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points—0
Teaching robots to perceive time -- A reinforcement learning approach (Extended version)—0
Temporal alignment and latent Gaussian process factor inference in population spike trains—0
Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding—0
Temporal Knowledge Graph Embedding based on Multivariate Gaussian Process—0
Tensor Regression Meets Gaussian Processes—0
The Automatic Statistician: A Relational Perspective—0
A Renormalization Group Approach to Connect Discrete- and Continuous-Time Descriptions of Gaussian Processes—0
The Elliptical Processes: a Family of Fat-tailed Stochastic Processes—0
The Fixed-b Limiting Distribution and the ERP of HAR Tests Under Nonstationarity—0
The Future is Log-Gaussian: ResNets and Their Infinite-Depth-and-Width Limit at Initialization—0
The Gaussian Process Latent Autoregressive Model—0
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning—0
The Human Kernel—0
The Limitations of Model Uncertainty in Adversarial Settings—0
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain—0
The Multivariate Generalised von Mises distribution: Inference and applications—0
Theoretical Analysis of Heteroscedastic Gaussian Processes with Posterior Distributions—0
The Price of Linear Time: Error Analysis of Structured Kernel Interpolation—0
The Promises and Pitfalls of Deep Kernel Learning—0
The Random Forest Kernel and other kernels for big data from random partitions—0
The Recycling Gibbs Sampler for Efficient Learning—0
The role of a layer in deep neural networks: a Gaussian Process perspective—0
The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface—0
The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning—0
The Use of Gaussian Processes in System Identification—0
The Impact of Data on the Stability of Learning-Based Control- Extended Version—0
Three-Dimensional Extended Object Tracking and Shape Learning Using Gaussian Processes—0
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory—0
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients—0
Tighter sparse variational Gaussian processes—0
Time-changed normalizing flows for accurate SDE modeling—0
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Benchmark Results

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