This paper considers stochastic optimization problems for a large class of objective
functions, including convex and continuous submodular. Stochastic proximal gradient …

It is generally believed that submodular functions–and the more general class of $\gamma $-
weakly submodular functions–may only be optimized under the non-negativity assumption …

Streaming algorithms are generally judged by the quality of their solution, memory footprint,
and computational complexity. In this paper, we study the problem of maximizing a …

Multiwinner voting rules are used to select a small representative subset of candidates or
items from a larger set given the preferences of voters. However, if candidates have …

Can we efficiently extract useful information from a large user-generated dataset while
protecting the privacy of the users and/or ensuring fairness in representation? We cast this …

The need for real time analysis of rapidly producing data streams (eg, video and image
streams) motivated the design of streaming algorithms that can efficiently extract and …

In this paper, we study the problem of constrained and stochastic continuous submodular
maximization. Even though the objective function is not concave (nor convex) and is defined …

In this paper, we develop the first one-pass streaming algorithm for submodular
maximization that does not evaluate the entire stream even once. By carefully subsampling …

Rule sets are highly interpretable logical models in which the predicates for decision are
expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model …

Balkanski and Singer [4] recently initiated the study of adaptivity (or parallelism) for
constrained submodular function maximization, and studied the setting of a cardinality …