Topics of Interest

Topics of interest include, but are not limited, to the following:

ML Quality:

- Software engineering aspects of ML systems and quality implications

- Testing of the quality of ML systems over time

- Debugging of ML systems

- Quality implication of ML algorithms on large-scale software systems

ML Reliability:

- Case studies of successful and unsuccessful applications of ML techniques

- Correctness of data abstraction, data trust

- Choice of ML techniques to meet security and quality

- Size of the training data, implied guaranties

- Application of classical statistics to ML systems quality

- Sensitivity to data distribution diversity and distribution drift

- The effect of labeling costs on solution quality (semi-supervised learning)

- Reliable transfer learning

ML Security:

- Vulnerability, sensitivity and attacks against ML

- Adversarial ML and adversary based learning models

- Strategy-proof ML algorithms