The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

In federated learning, multiple client devices jointly learn a machine learning model: each
client device maintains a local model for its local training dataset, while a master device …

Deep learning models have achieved high performance on many tasks, and thus have been
applied to many security-critical scenarios. For example, deep learning-based face …

As machine learning becomes widely used for automated decisions, attackers have strong
incentives to manipulate the results and models generated by machine learning algorithms …

Today's world is highly network interconnected owing to the pervasiveness of small personal
devices (eg, smartphones) as well as large computing devices or services (eg, cloud …

The advance in technologies such as e-commerce and financial technology (FinTech)
applications have sparked an increase in the number of online card transactions that occur …

This paper proposes a distributionally robust approach to logistic regression. We use the
Wasserstein distance to construct a ball in the space of probability distributions centered at …

Analysts often clean dirty data iteratively--cleaning some data, executing the analysis, and
then cleaning more data based on the results. We explore the iterative cleaning process in …