Outlier detection is essential to data analysis as it eliminates irregular or unforeseen values
that may affect the rest of a dataset. With the advent of Big Data, which produces copious …

Intelligent Applications (iApps), equipped with in-App deep learning (DL) models, are
emerging to provide reliable DL inference services. However, in-App DL models are …

Large Language Models (LLMs) enable a new ecosystem with many downstream
applications, called LLM applications, with different natural language processing tasks. The …

Counterfactual (CF) explanations for ML model predictions provide actionable recourse
recommendations to individuals adversely impacted by predicted outcomes. However …

The rise of Machine Learning as a Service (MLaaS) has led to the widespread deployment
of machine learning models trained on diverse datasets. These models are employed for …

Input transformation techniques have been proposed to defend against adversarial example
attacks in imageclassification systems. However, recent works have shown that, although …

Model extraction is to obtain a cloned model that replicates the functionality of a black-box
victim model solely through query-based access. Present defense strategies exhibit …