Xi Wu

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Google ∙ University of Wisconsin-Madison

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Xi Wu

Featured Co-authors

Kamalika Chaudhuri

Nicolas Papernot

Somesh Jha

Yingyu Liang

Ananthram Swami

Patrick McDaniel

Arun Kumar

Yifei Huang

Kassem Fawaz

Lingjiao Chen

Uyeong Jang

Robust Attribution Regularization

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

Manifold Assumption and Defenses Against Adversarial Perturbations

The Manifold Assumption and Defenses Against Adversarial Perturbations

Improving Adversarial Robustness by Data-Specific Discretization

Adversarial Learning and Explainability in Structured Datasets

Enhancing ML Robustness Using Physical-World Constraints

Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment