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09/30/2019
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
In this paper, we study the problem of constrained robust (min-max) opti...
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02/19/2019
There are No Bit Parts for Sign Bits in Black-Box Attacks
Machine learning models are vulnerable to adversarial examples. In this ...
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11/30/2018
Lipizzaner: A System That Scales Robust Generative Adversarial Network Training
GANs are difficult to train due to convergence pathologies such as mode ...
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11/14/2018
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episod
Timely prediction of clinically critical events in Intensive Care Unit (...
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10/03/2018
AST-Based Deep Learning for Detecting Malicious PowerShell
With the celebrated success of deep learning, some attempts to develop e...
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07/21/2018
Towards Distributed Coevolutionary GANs
Generative Adversarial Networks (GANs) have become one of the dominant m...
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05/15/2018
On the Application of Danskin's Theorem to Derivative-Free Minimax Optimization
Motivated by Danskin's theorem, gradient-based methods have been applied...
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05/09/2018
On Visual Hallmarks of Robustness to Adversarial Malware
A central challenge of adversarial learning is to interpret the resultin...
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04/27/2018
Approximating Nash Equilibria for Black-Box Games: A Bayesian Optimization Approach
Game theory has emerged as a powerful framework for modeling a large ran...
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01/09/2018
Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
Malware is constantly adapting in order to avoid detection. Model based ...
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10/14/2017
High Throughput 2D Spatial Image Filters on FPGAs
FPGAs are well established in the signal processing domain, where their ...
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11/27/2016
Embedded Bandits for Large-Scale Black-Box Optimization
Random embedding has been applied with empirical success to large-scale ...
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05/23/2016
