Recommender systems are personalized information access applications; they are
ubiquitous in today's online environment, and effective at finding items that meet user needs …

We study instancewise feature importance scoring as a method for model interpretation. Any
such method yields, for each predicted instance, a vector of importance scores associated …

In many fields, such as environmental risk assessment, agronomic system behavior,
aerospace engineering, and nuclear safety, mathematical models turned into computer code …

Game-theoretic attribution techniques based on Shapley values are used to interpret black-
box machine learning models, but their exact calculation is generally NP-hard, requiring …

We consider the problem of optimizing combinatorial spaces (eg, sequences, trees, and
graphs) using expensive black-box function evaluations. For example, optimizing molecules …

We consider the issue of strategic behaviour in various peer-assessment tasks, including
peer grading of exams or homeworks and peer review in hiring or promotions. When a peer …

Vibration-based damage detection relies on the observation of changes in damage-
sensitive dynamic features. However, a major problem is that dynamic features are sensitive …

This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs),
which are commonly observed in complex systems, eg, traffic, manufacturing, and weather …

Optimizing expensive to evaluate black-box functions over an input space consisting of all
permutations of d objects is an important problem with many real-world applications. For …

Abstract Mixture models of Plackett-Luce (PL), one of the most fundamental ranking models,
are an active research area of both theoretical and practical significance. Most previously …