Near-Optimal MAP Inference for Determinantal Point Processes

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How researchers see this paper

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Determinantal Point Processes for Machine Learning (2012, methodology): “In our recent work we showed how the computational structure of DPPs gives rise to a particularly efficient variant of these methods [81].”
Streaming submodular maximization: massive data summarization on the fly (2014, background): “Note that similar objective functions arise when performing MAP inference in Determinantal Point Processes, powerful probabilistic models for data summarization [13].”
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity (2017, methodology): “The gradient-based method in [17] and the double greedy algorithm in [10] are not compared because as reported in [20], they performed worse than ApX.”
Individualness and Determinantal Point Processes for Pedestrian Detection (2016, methodology): “Note that although there exists an approximate algorithm [21] for this problem, we use Algorithm 1, which has a formal guarantee for monotone submodular problems [8], since it is fast and works well in practice.”
Diverse Sequential Subset Selection for Supervised Video Summarization (2014, background): “However, there are several approaches to obtaining approximate solutions [13, 20].”
Constrained Dominant Sets for Multimodal Document Question Answering (2026, unknown): “MAP DPP is NP-hard, and standard pipelines revert to the same greedy approximation [14].”