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08/05/2019
Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment
With the industry trend of shifting from a traditional hierarchical appr...
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05/26/2019
Enhancing ML Robustness Using Physical-World Constraints
Recent advances in Machine Learning (ML) have demonstrated that neural n...
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05/23/2019
Robust Attribution Regularization
An emerging problem in trustworthy machine learning is to train models t...
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10/15/2018
Adversarial Learning and Explainability in Structured Datasets
We theoretically and empirically explore the explainability benefits of ...
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05/20/2018
Improving Adversarial Robustness by Data-Specific Discretization
A recent line of research proposed (either implicitly or explicitly) gra...
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11/21/2017
The Manifold Assumption and Defenses Against Adversarial Perturbations
In the adversarial-perturbation problem of neural networks, an adversary...
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11/21/2017
Manifold Assumption and Defenses Against Adversarial Perturbations
In the adversarial perturbation problem of neural networks, an adversary...
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02/22/2017
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...
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06/15/2016
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
While significant progress has been made separately on analytics systems...
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11/14/2015
