Forecasting SQL Query Cost at Twitter

1 code implementation • 12 Apr 2022 • Chunxu Tang, Beinan Wang, Zhenxiao Luo, Huijun Wu, Shajan Dasan, Maosong Fu, Yao Li, Mainak Ghosh, Ruchin Kabra, Nikhil Kantibhai Navadiya, Da Cheng, Fred Dai, Vrushali Channapattan, Prachi Mishra

We propose a SQL query cost predictor service, which employs machine learning techniques to train models from historical query request logs and rapidly forecasts the CPU and memory resource usages of online queries without any computation in a SQL engine.

Scheduling

CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph

no code implementations • 12 Sep 2021 • Xugang Wu, Huijun Wu, Xu Zhou, Kai Lu

Graph data, in most cases, has two views of information, namely structure information and feature information.

Adversarial Examples on Graph Data: Deep Insights into Attack and Defense

2 code implementations • 5 Mar 2019 • Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu

Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations.

Adversarial Attack Adversarial Defense

Interpreting Shared Deep Learning Models via Explicable Boundary Trees

no code implementations • 12 Sep 2017 • Huijun Wu, Chen Wang, Jie Yin, Kai Lu, Liming Zhu

In this paper, we propose a method to disclose a small set of training data that is just sufficient for users to get the insight of a complicated model.

Decision Making