Submodular streaming in all its glory: Tight approximation, minimum memory and low adaptive complexity
E Kazemi, M Mitrovic… - International …, 2019 - proceedings.mlr.press
Streaming algorithms are generally judged by the quality of their solution, memory footprint,
and computational complexity. In this paper, we study the problem of maximizing a …
and computational complexity. In this paper, we study the problem of maximizing a …
Constrained submodular maximization via new bounds for dr-submodular functions
N Buchbinder, M Feldman - Proceedings of the 56th Annual ACM …, 2024 - dl.acm.org
Submodular maximization under various constraints is a fundamental problem studied
continuously, in both computer science and operations research, since the late 1970's. A …
continuously, in both computer science and operations research, since the late 1970's. A …
Dynamic influence maximization
B Peng - Advances in Neural Information Processing …, 2021 - proceedings.neurips.cc
We initiate a systematic study on {\em dynamic influence maximization}(DIM). In the DIM
problem, one maintains a seed set $ S $ of at most $ k $ nodes in a dynamically involving …
problem, one maintains a seed set $ S $ of at most $ k $ nodes in a dynamically involving …
Practical parallel algorithms for submodular maximization subject to a knapsack constraint with nearly optimal adaptivity
Submodular maximization has wide applications in machine learning and data mining,
where massive datasets have brought the great need for designing efficient and …
where massive datasets have brought the great need for designing efficient and …
Non-monotone submodular maximization with nearly optimal adaptivity and query complexity
M Fahrbach, V Mirrokni… - … on Machine Learning, 2019 - proceedings.mlr.press
Submodular maximization is a general optimization problem with a wide range of
applications in machine learning (eg, active learning, clustering, and feature selection). In …
applications in machine learning (eg, active learning, clustering, and feature selection). In …
Batched dueling bandits
The K-armed dueling bandit problem, where the feedback is in the form of noisy pairwise
comparisons, has been widely studied. Previous works have only focused on the sequential …
comparisons, has been widely studied. Previous works have only focused on the sequential …
The FAST algorithm for submodular maximization
A Breuer, E Balkanski, Y Singer - … Conference on Machine …, 2020 - proceedings.mlr.press
In this paper we describe a new parallel algorithm called Fast Adaptive Sequencing
Technique (FAST) for maximizing a monotone submodular function under a cardinality …
Technique (FAST) for maximizing a monotone submodular function under a cardinality …
Dynamic algorithms for matroid submodular maximization
Submodular maximization under matroid and cardinality constraints are classical problems
with a wide range of applications in machine learning, auction theory, and combinatorial …
with a wide range of applications in machine learning, auction theory, and combinatorial …
Deterministic approximation for submodular maximization over a matroid in nearly linear time
We study the problem of maximizing a non-monotone, non-negative submodular function
subject to a matroid constraint. The prior best-known deterministic approximation ratio for …
subject to a matroid constraint. The prior best-known deterministic approximation ratio for …
The power of adaptivity for stochastic submodular cover
In the stochastic submodular cover problem, the goal is to select a subset of stochastic items
of minimum expected cost to cover a submodular function. Solutions in this setting …
of minimum expected cost to cover a submodular function. Solutions in this setting …