59 citations · 220 across the 28 of their papers we have counts for
7 papers · 1 filter
Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
Xubo Yue, Raed Al Kontar
In this paper, we propose \texttt{FGPR}: a Federated Gaussian process () regression framework that uses an averaging strategy for model aggregation and stochastic gra…
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Hao Chen, Lili Zheng, Raed Al Kontar +1
Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorithms for large-scale machine learning problems with independent samples due to the…
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes
Seokhyun Chung, Raed Al Kontar, Zhenke Wu
Multi-output regression seeks to borrow strength and leverage commonalities across different but related outputs in order to enhance learning and prediction accuracy. A fundamental…
The Renyi Gaussian Process: Towards Improved Generalization
Xubo Yue, Raed Kontar
We introduce an alternative closed form lower bound on the Gaussian process () likelihood based on the Rényi -divergence. This new lower bound can be viewed as a c…
Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data
Seokhyun Chung, Raed Kontar
The advance of modern sensor technologies enables collection of multi-stream longitudinal data where multiple signals from different units are collected in real-time. In this artic…
Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes
Xubo Yue, Raed Kontar
We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both longitudinal and time-to-event data. Our approach exploits a mu…
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach
Raed Kontar, Garvesh Raskutti, Shiyu Zhou
Recently there has been an increasing interest in the multivariate Gaussian process (MGP) which extends the Gaussian process (GP) to deal with multiple outputs. One approach to con…