Robust Submodular Observation Selection
By Andreas Krause, H. Brendan McMahan, Carlos Guestrin, Anupam Gupta. 2008
335 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/cd6fd2dca023cd98a4fee30c50e7855648cc27f6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- J. Bilmes
- Baharan Mirzasoleiman
- Rishabh K. Iyer
- Hannaneh Hajishirzi
- Ali Farhadi
- Mohammad Rastegari
- Hui-Ching Lin
- Yoshua Bengio
Citations in the corpus
- Submodular Function Maximization (2014, methodology): “Krause et al. (2008c) develop Saturate , an algorithm that is guaranteed to efficiently obtain a set S such that f min ( S ) ≥ OPT k , and | S | ≤ 1 + max v ln i f i ( { v } ) k .”
- Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints (2013, background): “SCSC and SCSK can in fact be extended to more flexible and complicated constraints which can arise naturally in many applications [18, 8].”
- Learning Mixtures of Submodular Shells with Application to Document Summarization (2012, background): “While they have a long history in operations research, game theory, econometrics, and electrical engineering, they are still beginning to be studied in machine learning for a variety of tasks including sensor placement [19, 20], structure learning of graphical models [34], document summarization [27”
- Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten" (2017, background): “Krause et al. (2008) proposed the problem of robust submodular observation selection, where we want to solve max|A|≤k mini∈[`] fi(A), for normalized monotonic fi.”
- A Class of Submodular Functions for Document Summarization (2011, methodology): “…learning and computer vision community (Kempe et al., 2003; Narasimhan and Bilmes, 2005; Krause and Guestrin, 2005; Narasimhan and Bilmes, 2007; Krause et al., 2008; Kolmogorov and Zabin, 2004) and have recently been introduced to natural language processing for the tasks of document…”
- Semantic Understanding of Professional Soccer Commentaries (2012, methodology): “Now we can adopt a greedy approximation of Equation 1 with reasonable error bounds [Goundan and Schulz, 2009; Dey et al., 2012; Krause et al., 2008].”