Most supervised learning models are trained for full automation. However, their predictions
are sometimes worse than those by human experts on some specific instances. Motivated by …

Constrained submodular maximization problems encompass a wide variety of applications,
including personalized recommendation, team formation, and revenue maximization via …

Gaussian processes (GPs) are non-parametric probabilistic regression models that are
popular due to their flexibility, data efficiency, and well-calibrated uncertainty estimates …

Submodularity is a discrete domain functional property that can be interpreted as mimicking
the role of well-known convexity/concavity properties in the continuous domain. Submodular …

An echo chamber effect describes the situation in which opinions are amplified by
communication and repetition inside a relatively closed social system. In this article, we will …

Subset selection, aiming to select the best subset from a ground set with respect to some
objective function, is a fundamental problem with applications in many areas, such as …

We study a pair of budget-and performance-constrained weak submodular maximization
problems. For computational efficiency, we explore the use of stochastic greedy algorithms …