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Found 8 papers, 3 papers with code
Gradient Reversal Against Discrimination
No methods currently exist for making arbitrary neural networks fair.
Non-Negative Networks Against Adversarial Attacks
Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available.
What About Applied Fairness?
Machine learning practitioners are often ambivalent about the ethical aspects of their products.
Engineering a Simplified 0-Bit Consistent Weighted Sampling
The Min-Hashing approach to sketching has become an important tool in data analysis, information retrial, and classification.
Fair Forests: Regularized Tree Induction to Minimize Model Bias
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.
Malware Detection by Eating a Whole EXE
In this work we introduce malware detection from raw byte sequences as a fruitful research area to the larger machine learning community.
Learning the PE Header, Malware Detection with Minimal Domain Knowledge
Many efforts have been made to use various forms of domain knowledge in malware detection.
Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media
There is a large amount of interest in understanding users of social media in order to predict their behavior in this space.
