Browse SoTA > Methodology > Gaussian Processes

Gaussian Processes

226 papers with code · Methodology

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

Benchmarks

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

Convolutional Gaussian Processes

NeurIPS 2017 pyro-ppl/pyro

We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images.

GAUSSIAN PROCESSES

Doubly Stochastic Variational Inference for Deep Gaussian Processes

NeurIPS 2017 pyro-ppl/pyro

Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.

GAUSSIAN PROCESSES VARIATIONAL INFERENCE

Exact Gaussian Processes on a Million Data Points

NeurIPS 2019 cornellius-gp/gpytorch

Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data.

GAUSSIAN PROCESSES

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

NeurIPS 2018 cornellius-gp/gpytorch

Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware.

GAUSSIAN PROCESSES

Product Kernel Interpolation for Scalable Gaussian Processes

24 Feb 2018cornellius-gp/gpytorch

Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs).

GAUSSIAN PROCESSES

Gaussian Processes for Big Data

26 Sep 2013cornellius-gp/gpytorch

We introduce stochastic variational inference for Gaussian process models.

GAUSSIAN PROCESSES LATENT VARIABLE MODELS VARIATIONAL INFERENCE

Adversarial Robustness Toolbox v1.0.0

3 Jul 2018IBM/adversarial-robustness-toolbox

Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.

GAUSSIAN PROCESSES TIME SERIES

Input Warping for Bayesian Optimization of Non-stationary Functions

5 Feb 2014HIPS/Spearmint

Bayesian optimization has proven to be a highly effective methodology for the global optimization of unknown, expensive and multimodal functions.

GAUSSIAN PROCESSES

Multi-Task Bayesian Optimization

NeurIPS 2013 HIPS/Spearmint

We demonstrate the utility of this new acquisition function by utilizing a small dataset in order to explore hyperparameter settings for a large dataset.

GAUSSIAN PROCESSES IMAGE CLASSIFICATION

A Framework for Interdomain and Multioutput Gaussian Processes

2 Mar 2020GPflow/GPflow

One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for small variations in the model or inference.

GAUSSIAN PROCESSES