We investigate the performance of the standard Greedy algorithm for cardinality constrained
maximization of non-submodular nondecreasing set functions. While there are strong …

With widespread use of machine learning methods in numerous domains involving humans,
several studies have raised questions about the potential for unfairness towards certain …

Prototypical examples that best summarize and compactly represent an underlying complex
data distribution, communicate meaningful insights to humans in domains where simple …

We connect high-dimensional subset selection and submodular maximization. Our results
extend the work of Das and Kempe [In ICML (2011) 1057–1064] from the setting of linear …

In many machine learning applications, it is important to explain the predictions of a black-
box classifier. For example, why does a deep neural network assign an image to a particular …

In this paper, we consider an online optimization process, where the objective functions are
not convex (nor concave) but instead belong to a broad class of continuous submodular …

Greedy algorithms are widely used for problems in machine learning such as feature
selection and set function optimization. Unfortunately, for large datasets, the running time of …

The problem of selecting the best $ k $-element subset from a universe is involved in many
applications. While previous studies assumed a noise-free environment or a noisy …

Importance Better prediction of major bleeding after percutaneous coronary intervention
(PCI) may improve clinical decisions aimed to reduce bleeding risk. Machine learning …

Requiring extensive human supervision is often impractical for continual learning due to its
cost, leading to the emergence of'name-only continual learning'that only provides the name …