Xi Wu

verfied profile

∙ 0 followers
Google ∙ University of Wisconsin-Madison

Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment

With the industry trend of shifting from a traditional hierarchical appr...
08/05/2019 ∙ by Yifei Huang, et al. ∙ 0 ∙ share

read it
Enhancing ML Robustness Using Physical-World Constraints

Recent advances in Machine Learning (ML) have demonstrated that neural n...
05/26/2019 ∙ by Varun Chandrasekaran, et al. ∙ 0 ∙ share

read it
Robust Attribution Regularization

An emerging problem in trustworthy machine learning is to train models t...
05/23/2019 ∙ by Jiefeng Chen, et al. ∙ 4 ∙ share

read it
Adversarial Learning and Explainability in Structured Datasets

We theoretically and empirically explore the explainability benefits of ...
10/15/2018 ∙ by Prasad Chalasani, et al. ∙ 0 ∙ share

read it
Improving Adversarial Robustness by Data-Specific Discretization

A recent line of research proposed (either implicitly or explicitly) gra...
05/20/2018 ∙ by Jiefeng Chen, et al. ∙ 0 ∙ share

read it
The Manifold Assumption and Defenses Against Adversarial Perturbations

In the adversarial-perturbation problem of neural networks, an adversary...
11/21/2017 ∙ by Xi Wu, et al. ∙ 0 ∙ share

read it
Manifold Assumption and Defenses Against Adversarial Perturbations

In the adversarial perturbation problem of neural networks, an adversary...
11/21/2017 ∙ by Xi Wu, et al. ∙ 0 ∙ share

read it
When Lempel-Ziv-Welch Meets Machine Learning: A Case Study of Accelerating Machine Learning using Coding

In this paper we study the use of coding techniques to accelerate machin...
02/22/2017 ∙ by Fengan Li, et al. ∙ 0 ∙ share

read it
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics

While significant progress has been made separately on analytics systems...
06/15/2016 ∙ by Xi Wu, et al. ∙ 0 ∙ share

read it
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

Deep learning algorithms have been shown to perform extremely well on ma...
11/14/2015 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

read it

Xi Wu

Featured Co-authors

Kamalika Chaudhuri

Nicolas Papernot

Somesh Jha

Ananthram Swami

Yingyu Liang

Patrick McDaniel

Arun Kumar

Yifei Huang

Kassem Fawaz

Varun Chandrasekaran

Lingjiao Chen

Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment

Enhancing ML Robustness Using Physical-World Constraints

Robust Attribution Regularization

Adversarial Learning and Explainability in Structured Datasets

Improving Adversarial Robustness by Data-Specific Discretization

The Manifold Assumption and Defenses Against Adversarial Perturbations

Manifold Assumption and Defenses Against Adversarial Perturbations

When Lempel-Ziv-Welch Meets Machine Learning: A Case Study of Accelerating Machine Learning using Coding

Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics

Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks