16 research outputs found
Advancing Subgroup Fairness via Sleeping Experts
We study methods for improving fairness to subgroups in settings with overlapping populations and sequential predictions. Classical notions of fairness focus on the balance of some property across different populations. However, in many applications the goal of the different groups is not to be predicted equally but rather to be predicted well. We demonstrate that the task of satisfying this guarantee for multiple overlapping groups is not straightforward and show that for the simple objective of unweighted average of false negative and false positive rate, satisfying this for overlapping populations can be statistically impossible even when we are provided predictors that perform well separately on each subgroup. On the positive side, we show that when individuals are equally important to the different groups they belong to, this goal is achievable; to do so, we draw a connection to the sleeping experts literature in online learning. Motivated by the one-sided feedback in natural settings of interest, we extend our results to such a feedback model. We also provide a game-theoretic interpretation of our results, examining the incentives of participants to join the system and to provide the system full information about predictors they may possess. We end with several interesting open problems concerning the strength of guarantees that can be achieved in a computationally efficient manner
Quantifying the Cost of Learning in Queueing Systems
Queueing systems are widely applicable stochastic models with use cases in
communication networks, healthcare, service systems, etc. Although their
optimal control has been extensively studied, most existing approaches assume
perfect knowledge of system parameters. Of course, this assumption rarely holds
in practice where there is parameter uncertainty, thus motivating a recent line
of work on bandit learning for queueing systems. This nascent stream of
research focuses on the asymptotic performance of the proposed algorithms.
In this paper, we argue that an asymptotic metric, which focuses on
late-stage performance, is insufficient to capture the intrinsic statistical
complexity of learning in queueing systems which typically occurs in the early
stage. Instead, we propose the Cost of Learning in Queueing (CLQ), a new metric
that quantifies the maximum increase in time-averaged queue length caused by
parameter uncertainty. We characterize the CLQ of a single-queue multi-server
system, and then extend these results to multi-queue multi-server systems and
networks of queues. In establishing our results, we propose a unified analysis
framework for CLQ that bridges Lyapunov and bandit analysis, which could be of
independent interest