Over the last few years, the Shapley value, a solution concept from cooperative game theory,
has found numerous applications in machine learning. In this paper, we first discuss …

This paper addresses the challenging task of weakly-supervised video temporal grounding.
Existing approaches are generally based on the moment proposal selection framework that …

In many multi-agent settings, participants can form teams to achieve collective outcomes that
may far surpass their individual capabilities. Measuring the relative contributions of agents …

Graph Neural Networks (GNNs) have been a powerful tool for node classification tasks in
complex networks. However, their decision-making processes remain a black-box to users …

Recommender systems based on Graph Neural Networks (GNN) have become the state-of-
the-art approach in recommendation, but they struggle with in extreme cold-start settings …

This paper discusses the importance of investigating DR using machine learning and a
computational method to rank DR risk factors by importance using different machine …

Explainability techniques are crucial in gaining insights into the reasons behind the
predictions of deep learning models, which have not yet been applied to chemical language …

We show how solution concepts from cooperative game theory can be used to tackle the
problem of pruning neural networks. The ever-growing size of deep neural networks (DNNs) …

The Shapley value has been proposed as a solution to many applications in machine
learning, including for equitable valuation of data. Shapley values are computationally …

Mathematical models of strategic interactions among rational agents have long been studied
in game theory. However the interactions studied are often over a small set of discrete …