6 papers · 1 filter
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…
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…
Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling
Skyler Wu, Fred Lu, Edward Raff +1
Online learning methods, like the seminal Passive-Aggressive (PA) classifier, are still highly effective for high-dimensional streaming data, out-of-core processing, and other thro…
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
Fred Lu, Ryan R. Curtin, Edward Raff +2
As the size of datasets used in statistical learning continues to grow, distributed training of models has attracted increasing attention. These methods partition the data and expl…
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification
Fred Lu, Ryan R. Curtin, Edward Raff +2
While distributed training is often viewed as a solution to optimizing linear models on increasingly large datasets, inter-machine communication costs of popular distributed approa…