About

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

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Datasets

Latest papers with code

How Bayesian Should Bayesian Optimisation Be?

3 May 2021georgedeath/how-bayesian-should-BO-be

FBBO using EI with an ARD kernel leads to the best performance in the noise-free setting, with much less difference between combinations of BO components when the noise is increased.

BAYESIAN OPTIMISATION GAUSSIAN PROCESSES

0
03 May 2021

GPflux: A Library for Deep Gaussian Processes

12 Apr 2021secondmind-labs/GPflux

GPflux is compatible with and built on top of the Keras deep learning eco-system.

GAUSSIAN PROCESSES

29
12 Apr 2021

Adversarial Robustness Guarantees for Gaussian Processes

7 Apr 2021andreapatane/check-GPclass

Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications.

GAUSSIAN PROCESSES

3
07 Apr 2021

Raven's Progressive Matrices Completion with Latent Gaussian Process Priors

22 Mar 2021Accelerator66/latent-gaussian-process-priors

In this paper we aim to solve the latter one by proposing a deep latent variable model, in which multiple Gaussian processes are employed as priors of latent variables to separately learn underlying abstract concepts from RPMs; thus the proposed model is interpretable in terms of concept-specific latent variables.

ANSWER SELECTION GAUSSIAN PROCESSES VISUAL REASONING

1
22 Mar 2021

Modelling the Multiwavelength Variability of Mrk 335 using Gaussian Processes

11 Mar 2021Ryan-Rhys/Mrk_335

Such a reprocessing model would be characterised by lags between X-ray and optical/UV emission due to differences in light travel time.

GAUSSIAN PROCESSES HIGH ENERGY ASTROPHYSICAL PHENOMENA

9
11 Mar 2021

Active Testing: Sample-Efficient Model Evaluation

9 Mar 2021jlko/active-testing

We introduce active testing: a new framework for sample-efficient model evaluation.

ACTIVE LEARNING GAUSSIAN PROCESSES

4
09 Mar 2021

Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data

4 Mar 2021PredictiveIntelligenceLab/GP-NODEs

This paper presents a machine learning framework (GP-NODE) for Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems.

BAYESIAN INFERENCE GAUSSIAN PROCESSES

5
04 Mar 2021

Fast Adaptation with Linearized Neural Networks

2 Mar 2021amzn/xfer

The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings.

DOMAIN ADAPTATION GAUSSIAN PROCESSES IMAGE CLASSIFICATION TRANSFER LEARNING

215
02 Mar 2021

Kernel Interpolation for Scalable Online Gaussian Processes

2 Mar 2021wjmaddox/online_gp

Gaussian processes (GPs) provide a gold standard for performance in online settings, such as sample-efficient control and black box optimization, where we need to update a posterior distribution as we acquire data in a sequential fashion.

GAUSSIAN PROCESSES

26
02 Mar 2021

Similarity measure for sparse time course data based on Gaussian processes

24 Feb 2021barahona-research-group/BayesFactorSimilarity

We propose a similarity measure for sparsely sampled time course data in the form of a log-likelihood ratio of Gaussian processes (GP).

GAUSSIAN PROCESSES TIME SERIES

1
24 Feb 2021