The recent explosion of interest in multimodal applications has resulted in a wide selection
of datasets and methods for representing and integrating information from different …

Partial Information Decomposition (PID) is a body of work within information theory that
allows one to quantify the information that several random variables provide about another …

In many machine learning systems that jointly learn from multiple modalities, a core research
question is to understand the nature of multimodal interactions: the emergence of new task …

Conventional federated learning (FL) assumes the homogeneity of models, necessitating
clients to expose their model parameters to enhance the performance of the server model …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Achieving fairness across diverse clients in Federated Learning (FL) remains a significant
challenge due to the heterogeneity of the data and the inaccessibility of sensitive attributes …

Spurious patterns refer to a mathematical association between two or more variables in a
dataset that are not causally related. However, this notion of spuriousness, which is usually …

As federated learning gains increasing importance in real-world applications due to its
capacity for decentralized data training, addressing fairness concerns across demographic …

Group fairness in machine learning is a critical area of research focused on achieving
equitable outcomes across different groups defined by sensitive attributes such as race or …

The partial information decomposition (PID) framework is concerned with decomposing the
information that a set of (two or more) random variables (the sources) has about another …