CONTEXT: Applying vulnerability detection techniques is one of many task...
Background: Most of the existing machine learning models for security ta...
Background: Machine learning techniques have been widely used and demons...
High-dimensional observations are a major challenge in the application o...
Lack of security expertise among software practitioners is a problem wit...
BACKGROUND: Machine learning-based security detection models have become...
Software developed on public platforms are a source of data that can be ...
High-dimensional observations and unknown dynamics are major challenges ...
Many methods have been proposed to estimate how much effort is required ...
Background: Security bugs need to be handled by small groups of engineer...
Real-world datasets are often biased with respect to key demographic fac...
Learning disentangled representations that correspond to factors of vari...
Many real-world sequential decision-making problems can be formulated as...
Given unpaired data from multiple domains, a key challenge is to efficie...
When security bugs are detected, they should be (a) discussed privately ...
The recognition network in deep latent variable models such as variation...
The variational autoencoder (VAE) is a popular model for density estimat...
Adversarial examples are typically constructed by perturbing an existing...
Domain adaptation refers to the problem of leveraging labeled data in a
...
In this work, we employ quantitative methods from the realm of statistic...