38 citations · 104 across the 8 of their papers we have counts for
9 papers
Holistic Cube Analysis: A Query Framework for Data Insights
Xi Wu, Shaleen Deep, Joe Benassi +11
Many data insight questions can be viewed as searching in a large space of tables and finding important ones, where the notion of importance is defined in some adhoc user defined m…
Robust Attribution Regularization
Jiefeng Chen, Xi Wu, Vaibhav Rastogi +2
An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions. We take a step towards solving this problem throug…
Concise Explanations of Neural Networks using Adversarial Training
Prasad Chalasani, Jiefeng Chen, Amrita Roy Chowdhury +2
We show new connections between adversarial learning and explainability for deep neural networks (DNNs). One form of explanation of the output of a neural network model in terms of…
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Xi Wu, Uyeong Jang, Jiefeng Chen +2
In this paper we study leveraging confidence information induced by adversarial training to reinforce adversarial robustness of a given adversarially trained model. A natural measu…
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
Xi Wu, Fengan Li, Arun Kumar +3
While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics framew…
Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences
Xi Wu, Matthew Fredrikson, Wentao Wu +2
Private regression has received attention from both database and security communities. Recent work by Fredrikson et al. (USENIX Security 2014) analyzed the functional mechanism (Zh…
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Nicolas Papernot, Patrick McDaniel, Xi Wu +2
Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other ma…
Uncertainty Aware Query Execution Time Prediction
Wentao Wu, Xi Wu, Hakan Hacıgümüş +1
Predicting query execution time is a fundamental issue underlying many database management tasks. Existing predictors rely on information such as cardinality estimates and system p…
Hierarchies of Inefficient Kernelizability
Danny Hermelin, Stefan Kratsch, Karolina Sołtys +2
The framework of Bodlaender et al. (ICALP 2008) and Fortnow and Santhanam (STOC 2008) allows us to exclude the existence of polynomial kernels for a range of problems under reasona…