Outlier detection and novelty detection are two important topics for anomaly detection.
Suppose the majority of a dataset are drawn from a certain distribution, outlier detection and …

In recent years, the use of smart devices has increased exponentially, resulting in massive
amounts of data. To handle this data, effective data storage and management has required …

Deep learning-based language models have achieved state-of-the-art results in a number of
applications including sentiment analysis, topic labelling, intent classification and others …

Differential privacy (DP) is a gold-standard concept of measuring and guaranteeing privacy
in data analysis. It is well-known that the cost of adding DP to deep learning model is its …

Adversarial training can considerably robustify deep neural networks to resist adversarial
attacks. However, some works suggested that adversarial training might comprise the …

We introduce a framework for dynamic adversarial discovery of information (DADI),
motivated by a scenario where information (a feature set) is used by third parties with …

In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in
lifelong learning (L2M) for deep neural networks. Our key idea is to employ functional …

Adversarial example attacks twist an image to cause image classifiers to output a wrong
prediction, yet the perturbation is too subtle to be perceived by a human. Existing research …

This dissertation assesses how artificial neural networks (ANNs) and other machine learning
systems should be devised, built, and implemented in US governmental organizations (ie …

The last five years have seen a vast increase in academic and popular interest in" fair"
machine learning. But while the community has made significant progress towards …