Improving Certified Robustness via Statistical Learning with Logical Reasoning

1 code implementation • 28 Feb 2020 • Zhuolin Yang, Zhikuan Zhao, Boxin Wang, Jiawei Zhang, Linyi Li, Hengzhi Pei, Bojan Karlas, Ji Liu, Heng Guo, Ce Zhang, Bo Li

Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently.

BIG-bench Machine Learning Logical Reasoning

Data Science through the looking glass and what we found there

no code implementations • 19 Dec 2019 • Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer

The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners.

AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI

no code implementations • 17 Mar 2018 • Chen Yu, Bojan Karlas, Jie Zhong, Ce Zhang, Ji Liu

In this paper, we focus on the AutoML problem from the \emph{service provider's perspective}, motivated by the following practical consideration: When an AutoML service needs to serve {\em multiple users} with {\em multiple devices} at the same time, how can we allocate these devices to users in an efficient way?

AutoML Model Selection