9 accepted papers
Estimating the true prevalence of an epidemic outbreak is a key public health problem. This is challenging because surveillance is usually resource intensive and biased. In the network setting, prior work on cost sensitive disease surveillance has focused on choosing a subset of individuals (or node
As AI algorithms are deployed extensively, the need to ensure the fairness of their outputs is critical. Most existing work is on “fairness by design” approaches that incorporate limited tests for fairness into a limited number of algorithms. Here, we explore a framework that removes these limitatio…
Networked dynamical systems are widely used as formal models of real-world cascading phenomena, such as the spread of diseases and information. Prior research has addressed the problem of learning the behavior of an unknown dynamical system when the underlying network has a single layer. In this wor…
Discrete dynamical systems are commonly used to model the spread of contagions on real-world networks. Under the PAC framework, existing research has studied the problem of learning the behavior of a system, assuming that the underlying network is known. In this work, we focus on a more challenging…
Evolutionary anti-coordination games on networks capture real-world strategic situations such as traffic routing and market competition. Two key problems concerning evolutionary games are the existence of a pure Nash equilibrium (NE) and the convergence time. In this work, we study these two problem…
Applications such as employees sharing office spaces over a workweek can be modeled as problems where agents are matched to resources over multiple rounds. Agents' requirements limit the set of compatible resources and the rounds in which they want to be matched. Viewing such an application as a mu…
Using a discrete dynamical system model, many papers have addressed the problem of learning the behavior (i.e., the local function at each node) of a networked system through active queries, assuming that the network topology is known. We address the problem of inferring both the topology of the net…
Discrete dynamical systems serve as useful formal models to study diffusion phenomena in social networks. Motivated by applications in systems biology, several recent papers have studied algorithmic and complexity aspects of diffusion problems for dynamical systems whose underlying graphs are direc…
In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used t…