Stochastic conditional gradient methods: From convex minimization to submodular maximization
This paper considers stochastic optimization problems for a large class of objective
functions, including convex and continuous submodular. Stochastic proximal gradient …
functions, including convex and continuous submodular. Stochastic proximal gradient …
Submodular maximization beyond non-negativity: Guarantees, fast algorithms, and applications
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 …
weakly submodular functions–may only be optimized under the non-negativity assumption …
Submodular streaming in all its glory: Tight approximation, minimum memory and low adaptive complexity
E Kazemi, M Mitrovic… - International …, 2019 - proceedings.mlr.press
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 …
and computational complexity. In this paper, we study the problem of maximizing a …
Multiwinner voting with fairness constraints
LE Celis, L Huang, NK Vishnoi - arXiv preprint arXiv:1710.10057, 2017 - arxiv.org
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 …
items from a larger set given the preferences of voters. However, if candidates have …
Scalable deletion-robust submodular maximization: Data summarization with privacy and fairness constraints
E Kazemi, M Zadimoghaddam… - … conference on machine …, 2018 - proceedings.mlr.press
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 …
protecting the privacy of the users and/or ensuring fairness in representation? We cast this …
Streaming non-monotone submodular maximization: Personalized video summarization on the fly
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 …
streams) motivated the design of streaming algorithms that can efficiently extract and …
Conditional gradient method for stochastic submodular maximization: Closing the gap
A Mokhtari, H Hassani… - … Conference on Artificial …, 2018 - proceedings.mlr.press
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 …
maximization. Even though the objective function is not concave (nor convex) and is defined …
Do less, get more: Streaming submodular maximization with subsampling
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 …
maximization that does not evaluate the entire stream even once. By carefully subsampling …
Learning interpretable decision rule sets: A submodular optimization approach
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 …
expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model …
Submodular function maximization in parallel via the multilinear relaxation
Balkanski and Singer [4] recently initiated the study of adaptivity (or parallelism) for
constrained submodular function maximization, and studied the setting of a cardinality …
constrained submodular function maximization, and studied the setting of a cardinality …