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Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
In the present paper, we bridge this gap, by proposing several new algorithms (including greedy, majorization-minimization, minorization-maximization, and relaxation algorithms) that not only scale to large datasets but that also achieve theoretical approximation guarantees comparable to the state-of-the-art.
Learning Mixtures of Submodular Functions for Image Collection Summarization
This paper provides, to our knowledge, the first systematic approach for quantifying the problem of image collection summarization, along with a new dataset of image collections and human summaries.
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
We are motivated by a number of real-world applications in machine learning including sensor placement and data subset selection, which require maximizing a certain submodular function (like coverage or diversity) while simultaneously minimizing another (like cooperative cost).
