Beyond 1/2-approximation for submodular maximization on massive data streams
A Norouzi-Fard, J Tarnawski… - International …, 2018 - proceedings.mlr.press
Many tasks in machine learning and data mining, such as data diversification, non-
parametric learning, kernel machines, clustering etc., require extracting a small but …
parametric learning, kernel machines, clustering etc., require extracting a small but …
Fairness in streaming submodular maximization: Algorithms and hardness
M El Halabi, S Mitrović… - Advances in …, 2020 - proceedings.neurips.cc
Submodular maximization has become established as the method of choice for the task of
selecting representative and diverse summaries of data. However, if datapoints have …
selecting representative and diverse summaries of data. However, if datapoints have …
Streaming robust submodular maximization: A partitioned thresholding approach
S Mitrovic, I Bogunovic… - Advances in …, 2017 - proceedings.neurips.cc
We study the classical problem of maximizing a monotone submodular function subject to a
cardinality constraint k, with two additional twists:(i) elements arrive in a streaming fashion …
cardinality constraint k, with two additional twists:(i) elements arrive in a streaming fashion …
Federated submodular maximization with differential privacy
Submodular maximization is a fundamental problem in many Internet of Things applications,
such as sensor placement, resource allocation, and mobile crowdsourcing. Despite being …
such as sensor placement, resource allocation, and mobile crowdsourcing. Despite being …
Sparsification of decomposable submodular functions
A Rafiey, Y Yoshida - Proceedings of the AAAI Conference on Artificial …, 2022 - ojs.aaai.org
Submodular functions are at the core of many machine learning and data mining tasks. The
underlying submodular functions for many of these tasks are decomposable, ie, they are …
underlying submodular functions for many of these tasks are decomposable, ie, they are …
Using statistical measures and machine learning for graph reduction to solve maximum weight clique problems
In this article, we investigate problem reduction techniques using stochastic sampling and
machine learning to tackle large-scale optimization problems. These techniques …
machine learning to tackle large-scale optimization problems. These techniques …
“bring your own greedy”+ max: near-optimal 1/2-approximations for submodular knapsack
G Yaroslavtsev, S Zhou… - … Conference on Artificial …, 2020 - proceedings.mlr.press
The problem of selecting a small-size representative summary of a large dataset is a
cornerstone of machine learning, optimization and data science. Motivated by applications …
cornerstone of machine learning, optimization and data science. Motivated by applications …
Fair and representative subset selection from data streams
We study the problem of extracting a small subset of representative items from a large data
stream. In many data mining and machine learning applications such as social network …
stream. In many data mining and machine learning applications such as social network …
Instance specific approximations for submodular maximization
E Balkanski, S Qian, Y Singer - International Conference on …, 2021 - proceedings.mlr.press
The predominant measure for the performance of an algorithm is its worst-case
approximation guarantee. While worst-case approximations give desirable robustness …
approximation guarantee. While worst-case approximations give desirable robustness …
Balancing Utility and Fairness in Submodular Maximization (Technical Report)
Submodular function maximization is a fundamental combinatorial optimization problem with
plenty of applications--including data summarization, influence maximization, and …
plenty of applications--including data summarization, influence maximization, and …