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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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Latest papers without code

Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression

6 May 2021

However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required.

GAUSSIAN PROCESSES

Numerical Gaussian process Kalman filtering for spatiotemporal systems

5 May 2021

These properties enable us to embed the numerical GP state space model into the recursive Kalman filter algorithm.

GAUSSIAN PROCESSES PHYSICS-INFORMED MACHINE LEARNING

Fractional Barndorff-Nielsen and Shephard model: applications in variance and volatility swaps, and hedging

5 May 2021

The model is analyzed in connection to the quadratic hedging problem and some related analytical results are developed.

GAUSSIAN PROCESSES

MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validation

29 Apr 2021

Gaussian processes (GPs) are non-linear probabilistic models popular in many applications.

GAUSSIAN PROCESSES

Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation

28 Apr 2021

We propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty.

GAUSSIAN PROCESSES OUTLIER DETECTION OUT-OF-DISTRIBUTION DETECTION SEMANTIC SEGMENTATION

Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes

26 Apr 2021

Using this representation, we show that the Sinkhorn divergence between two centered Gaussian processes can be consistently and efficiently estimated from the divergence between their corresponding normalized finite-dimensional covariance matrices, or alternatively, their sample covariance operators.

GAUSSIAN PROCESSES

One-parameter family of acquisition functions for efficient global optimization

26 Apr 2021

Bayesian optimization (BO) with Gaussian processes is a powerful methodology to optimize an expensive black-box function with as few function evaluations as possible.

GAUSSIAN PROCESSES GLOBAL OPTIMIZATION

Safe Chance Constrained Reinforcement Learning for Batch Process Control

23 Apr 2021

Specifically, we propose a data-driven approach that utilizes Gaussian processes for the offline simulation model and use the associated posterior uncertainty prediction to account for joint chance constraints and plant-model mismatch.

GAUSSIAN PROCESSES

Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems

23 Apr 2021

Instead, we propose performing BO on complex, structured problems by using Bayesian Neural Networks (BNNs), a class of scalable surrogate models that have the representation power and flexibility to handle structured data and exploit auxiliary information.

GAUSSIAN PROCESSES GLOBAL OPTIMIZATION

A Gaussian Process Model of Cross-Category Dynamics in Brand Choice

23 Apr 2021

Moreover, we find that capturing correlated dynamics can have implications for understanding changes in consumers preferences over time, and developing targeted marketing strategies based on those dynamics.

GAUSSIAN PROCESSES