As the operations of autonomous systems generally affect simultaneously several users, it is
crucial that their designs account for fairness considerations. In contrast to standard (deep) …

Customizing robotic behaviors to be aligned with diverse human preferences is an
underexplored challenge in the field of embodied AI. In this paper we present Promptable …

While the whole society is meant to benefit from sustainable development; environmental
and social fairness considerations are often overlooked in the design of supply chain …

Recommender systems powering online multi-stakeholder platforms often face the
challenge of jointly optimizing multiple objectives, in an attempt to efficiently match suppliers …

We consider the problem of learning fair policies in (deep) cooperative multi-agent
reinforcement learning (MARL). We formalize it in a principled way as the problem of …

There is growing interest in designing recommender systems that aim at being fair towards
item producers or their least satisfied users. Inspired by the domain of inequality …

The main goal of this paper is to introduce the reader to the multiarmed bandit algorithms of
different types and to observe how the industry leveraged them in advancing …

Bayesian optimization (BO) is a popular tool for optimizing complex and costly-to-evaluate
black-box objective functions. To further reduce the number of function evaluations, any …

Ad-load balancing is a critical challenge in online advertising systems, particularly in the
context of social media platforms, where the goal is to maximize user engagement and …

We consider an agent who is involved in an online Markov decision process, and receives a
vector of outcomes every round. The agent aims to simultaneously optimize multiple …