5 papers
Adversarial Samples Are Not Created Equal
Jennifer Crawford, Amol Khanna, Fred Lu +4
Over the past decade, numerous theories have been proposed to explain the widespread vulnerability of deep neural networks to adversarial evasion attacks. Among these, the theory o…
Intermediate N-Gramming: Deterministic and Fast N-Grams For Large N and Large Datasets
Ryan R. Curtin, Fred Lu, Edward Raff +1
The number of n-gram features grows exponentially in n, making it computationally demanding to compute the most frequent n-grams even for n as small as 3. Motivated by our producti…
Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms
Derek Everett, Fred Lu, Edward Raff +2
Canonical algorithms for multi-armed bandits typically assume a stationary reward environment where the size of the action space (number of arms) is small. More recently developed…
Differentially Private Iterative Screening Rules for Linear Regression
Amol Khanna, Fred Lu, Edward Raff
Linear -regularized models have remained one of the simplest and most effective tools in data science. Over the past decade, screening rules have risen in popularity as a way…
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection
Siddhant Gupta, Fred Lu, Andrew Barlow +5
A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious acto…