Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

We review and reflect on fairness notions proposed in machine learning literature and make
an attempt to draw connections to arguments in moral and political philosophy, especially …

The multi-armed bandit (MAB) model has been widely adopted for studying many practical
optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with …

Although many fairness criteria have been proposed for decision making, their long-term
impact on the well-being of a population remains unclear. In this work, we study the …

Contextual bandit algorithms have become widely used for recommendation in online
systems (eg marketplaces, music streaming, news), where they now wield substantial …

Randomized experiments have been the gold standard for assessing the effectiveness of a
treatment, policy, or intervention, spanning various fields, including social sciences …

While interest in the application of machine learning to improve healthcare has grown
tremendously in recent years, a number of barriers prevent deployment in medical practice …

When an AI system interacts with multiple users, it frequently needs to make allocation
decisions. For instance, a virtual agent decides whom to pay attention to in a group, or a …

This paper proposes and studies for the first time the problem of combinatorial multi-armed
bandits with linear long-term constraints. Our model generalizes and unifies several …

How should a robot that collaborates with multiple people decide upon the distribution of
resources (eg social attention, or parts needed for an assembly)? People are uniquely …