133 citations · 179 across the 7 of their papers we have counts for
8 papers
Gradient Reversal Against Discrimination
Edward Raff, Jared Sylvester
No methods currently exist for making arbitrary neural networks fair. In this work we introduce GRAD, a new and simplified method to producing fair neural networks that can be used…
What About Applied Fairness?
Jared Sylvester, Edward Raff
Machine learning practitioners are often ambivalent about the ethical aspects of their products. We believe anything that gets us from that current state to one in which our system…
Non-Negative Networks Against Adversarial Attacks
William Fleshman, Edward Raff, Jared Sylvester +2
Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available. We make progress toward this probl…
Engineering a Simplified 0-Bit Consistent Weighted Sampling
Edward Raff, Jared Sylvester, Charles Nicholas
The Min-Hashing approach to sketching has become an important tool in data analysis, information retrial, and classification. To apply it to real-valued datasets, the ICWS algorith…
Fair Forests: Regularized Tree Induction to Minimize Model Bias
Edward Raff, Jared Sylvester, Steven Mills
The potential lack of fairness in the outputs of machine learning algorithms has recently gained attention both within the research community as well as in society more broadly. Su…
Malware Detection by Eating a Whole EXE
Edward Raff, Jon Barker, Jared Sylvester +3
In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community. Building a neural network for such a probl…