Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten"

By Baharan Mirzasoleiman, Amin Karbasi, Andreas Krause. 2017

94 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Alessandro Achille
2. Aditya Golatkar
3. Stefano Soatto
4. Avinash Ravichandran
5. Marzia Polito
6. Han-Jia Ye
7. Pratik Chaudhari
8. Shuyang Gao

Citations in the corpus

• Mixed-Privacy Forgetting in Deep Networks (2020, methodology): “[32, 13] gives method for forgetting for particular class of learning algorithms, such as k-means clustering.”
• Submodular Maximization with Nearly Optimal Approximation, Adaptivity and Query Complexity (2018, unknown): “…on the setting where the submodular maximization algorithms are applied, new challenges emerge and hence more practical algorithms have been designed to solve the problem in distributed [MKSK13, MZ15, BENW15], streaming [BMKK14], and robust [MKK17, MBNF + 17, KZK18] optimization frameworks.”
• Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations (2020, methodology): “[30] provided the first framework for instantaneous data summarization with data deletion using robust streaming submodular optimization.”