Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Yue, Xubo"'
Optimal design is a critical yet challenging task within many applications. This challenge arises from the need for extensive trial and error, often done through simulations or running field experiments. Fortunately, sequential optimal design, also r
Externí odkaz:
http://arxiv.org/abs/2306.14348
Publikováno v:
IISE Transactions, 2023
As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge compute resources are exploited to process more of the data locally. This regime of analytics is coined as feder
Externí odkaz:
http://arxiv.org/abs/2206.07786
Physics-informed Neural Networks (PINNs) are gaining attention in the engineering and scientific literature for solving a range of differential equations with applications in weather modeling, healthcare, manufacturing, etc. Poor scalability is one o
Externí odkaz:
http://arxiv.org/abs/2204.12589
Autor:
Yue, Xubo, Kontar, Raed Al
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
In this paper, we propose \texttt{FGPR}: a Federated Gaussian process ($\mathcal{GP}$) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local client computations. Notably, the resulting gl
Externí odkaz:
http://arxiv.org/abs/2111.14008
Autor:
Kontar, Raed, Shi, Naichen, Yue, Xubo, Chung, Seokhyun, Byon, Eunshin, Chowdhury, Mosharaf, Jin, Judy, Kontar, Wissam, Masoud, Neda, Noueihed, Maher, Okwudire, Chinedum E., Raskutti, Garvesh, Saigal, Romesh, Singh, Karandeep, Ye, Zhisheng
Publikováno v:
IEEE Access, 2021
The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge, allowing IoT devices to collaboratively extract kno
Externí odkaz:
http://arxiv.org/abs/2111.05326
Publikováno v:
Informs Journal on Data Science, 2022
In this paper we propose \texttt{GIFAIR-FL}: a framework that imposes \textbf{G}roup and \textbf{I}ndividual \textbf{FAIR}ness to \textbf{F}ederated \textbf{L}earning settings. By adding a regularization term, our algorithm penalizes the spread in th
Externí odkaz:
http://arxiv.org/abs/2108.02741
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 2023
In an effort to improve generalization in deep learning and automate the process of learning rate scheduling, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers. Our method dynamically updates the le
Externí odkaz:
http://arxiv.org/abs/2011.05348
Autor:
Yue, Xubo, Kontar, Raed Al
Publikováno v:
Artificial Intelligence and Statistics (AISTATS) 2020
Lookahead, also known as non-myopic, Bayesian optimization (BO) aims to find optimal sampling policies through solving a dynamic program (DP) that maximizes a long-term reward over a rolling horizon. Though promising, lookahead BO faces the risk of e
Externí odkaz:
http://arxiv.org/abs/1911.01004
Autor:
Yue, Xubo, Kontar, Raed
Publikováno v:
IISE Transactions, 2023
We introduce an alternative closed form lower bound on the Gaussian process ($\mathcal{GP}$) likelihood based on the R\'enyi $\alpha$-divergence. This new lower bound can be viewed as a convex combination of the Nystr\"om approximation and the exact
Externí odkaz:
http://arxiv.org/abs/1910.06990
Autor:
Yue, Xubo, Kontar, Raed
Publikováno v:
Technometrics, 2020
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 multivariate Gaussian convolution process (MGCP) to model the evolution
Externí odkaz:
http://arxiv.org/abs/1903.03867