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 consider the problem of online fair division of indivisible goods to players when there are
a finite number of types of goods and player values are drawn from distributions with …

The widespread integration of Machine Learning systems in daily life, particularly in high-
stakes domains, has raised concerns about the fairness implications. While prior works have …

Bias issues of neural networks garner significant attention along with its promising
advancement. Among various bias issues, mitigating two predominant biases is crucial in …

The safe linear bandit problem is a version of the classical stochastic linear bandit problem
where the learner's actions must satisfy an uncertain constraint at all rounds. Due its …

Minimizing social bias strengthens societal bonds, promoting shared understanding and
better decision-making. We revisit the definition of bias by discovering new bias types (eg …

In this work, we present a novel framework for Best Arm Identification (BAI) under fairness
constraints, a setting that we refer to as\textit {F-BAI}(fair BAI). Unlike traditional BAI, which …

Today's online platforms heavily lean on algorithmic recommendations for bolstering user
engagement and driving revenue. However, these recommendations can impact multiple …

Existing approaches to fairness in stochastic multi-armed bandits (MAB) primarily focus on
exposure guarantee to individual arms. When arms are naturally grouped by certain attribute …

This paper considers a novel online fair division problem involving multiple agents in which
a learner observes an indivisible item that has to be irrevocably allocated to one of the …