1 code implementation • 3 Feb 2022 • Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang
As a result, the wider application of these techniques is currently limited by its scalability and flexibility -- these techniques often do not scale to large-scale datasets with modern deep neural networks or cannot handle loss functions which may be non-smooth such as the 0-1 loss.
no code implementations • 21 Sep 2020 • Maurice Weber, Nana Liu, Bo Li, Ce Zhang, Zhikuan Zhao
This link leads to a tight robustness condition which puts constraints on the amount of noise a classifier can tolerate, independent of whether the noise source is natural or adversarial.
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.
2 code implementations • 29 Jun 2018 • Zhikuan Zhao, Alejandro Pozas-Kerstjens, Patrick Rebentrost, Peter Wittek
Furthermore, we demonstrate the execution of the algorithm on contemporary quantum computers and analyze its robustness with respect to realistic noise models.
no code implementations • 1 Apr 2018 • Zhikuan Zhao, Jack K. Fitzsimons, Patrick Rebentrost, Vedran Dunjko, Joseph F. Fitzsimons
Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage.
no code implementations • 28 Mar 2018 • Zhikuan Zhao, Jack K. Fitzsimons, Michael A. Osborne, Stephen J. Roberts, Joseph F. Fitzsimons
Gaussian processes (GPs) are important models in supervised machine learning.
no code implementations • 12 Dec 2015 • Zhikuan Zhao, Jack K. Fitzsimons, Joseph F. Fitzsimons
We show that even in some cases not ideally suited to the quantum linear systems algorithm, a polynomial increase in efficiency still occurs.