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 1601–1650 of 1963 papers

TitleStatusHype
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact—0
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces—0
Adaptive Sensing for Learning Nonstationary Environment Models—0
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data—0
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio—0
Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems—0
Quantum algorithms for training Gaussian Processes—0
Scalable Generalized Dynamic Topic ModelsCode0
Meta Reinforcement Learning with Latent Variable Gaussian Processes—0
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process—0
Asymmetric kernel in Gaussian Processes for learning target variance—0
Gaussian Processes indexed on the symmetric group: prediction and learning—0
Constant-Time Predictive Distributions for Gaussian ProcessesCode0
Gaussian Processes Over Graphs—0
Variational zero-inflated Gaussian processes with sparse kernelsCode0
Learning unknown ODE models with Gaussian processesCode0
Dimension-Robust MCMC in Bayesian Inverse Problems—0
Conditionally Independent Multiresolution Gaussian ProcessesCode0
Product Kernel Interpolation for Scalable Gaussian Processes—0
Machine learning based hyperspectral image analysis: A survey—0
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector CalculusCode0
Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)Code0
VBALD - Variational Bayesian Approximation of Log Determinants—0
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision MakingCode0
Learning Integral Representations of Gaussian ProcessesCode0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Emulating dynamic non-linear simulators using Gaussian processes—0
The Gaussian Process Autoregressive Regression Model (GPAR)Code1
Analysis of Financial Credit Risk Using Machine Learning—0
Prophit: Causal inverse classification for multiple continuously valued treatment policies—0
State Space Gaussian Processes with Non-Gaussian Likelihood—0
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer—0
Practical Transfer Learning for Bayesian OptimizationCode0
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks—0
The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface—0
Scalable Lévy Process Priors for Spectral Kernel LearningCode0
Composite Gaussian Processes: Scalable Computation and Performance Analysis—0
Kernel Distillation for Fast Gaussian Processes Prediction—0
Probabilistic Recurrent State-Space ModelsCode1
Algorithmic Linearly Constrained Gaussian Processes—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Upgrading from Gaussian Processes to Student's-T Processes—0
Hyperspectral recovery from RGB images using Gaussian Processes—0
Deep Gaussian Processes with Decoupled Inducing Inputs—0
Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge"Code0
Multiscale Sparse Microcanonical Models—0
PHOENICS: A universal deep Bayesian optimizerCode0
Intrinsic Gaussian processes on complex constrained domains—0
Gaussian Process Neurons—0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks—0
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Benchmark Results

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