Results 131 to 140 of about 1,909,109 (209)

Distributed Submodular Minimization And Motion Coordination Over Discrete State Space

, 2017
Submodular set-functions are extensively used in large-scale combinatorial optimization problems arising in complex networks and machine learning. While there has been significant interest in distributed formulations of convex optimization, distributed ...
Shamma, Jeff S., Jaleel, Hassan
core  

Hypergraphs with edge-dependent vertex weights: p-Laplacians and spectral clustering. [PDF]

Front Big Data, 2023
Zhu Y, Segarra S.
europepmc   +1 more source

Submodular Optimization with Contention Resolution Extensions.

, 2019
This paper considers optimizing a submodular function subject to a set of downward closed constraints. Previous literature on this problem has often constructed solutions by (1) discovering a fractional solution to the multi-linear extension and (2) rounding this solution to an integral solution via a contention resolution scheme. This line of research
Benjamin Moseley, Maxim Sviridenko
openaire   +3 more sources

Primer design through submodular function estimation. [PDF]

Bioinformatics
Chen Y, Han Y, Wang A, Hong A, Rivers AR, Kuhnle A, Boucher C.   +6 more
europepmc   +1 more source

Multilinear Extensions in Submodular Optimization for Optimal Sensor Scheduling in Nonlinear Networks [PDF]

Optimal sensing nodes selection in dynamic systems is a combinatorial optimization problem that has been thoroughly studied in the recent literature. This problem can be formulated within the context of set optimization.
Kazma, Mohamad H., Taha, Ahmad F.
core  

Threshold-Aware Link-Graph Compression with Per-Sensor Observed-Packet Coverage Constraints and Temporal Robustness in Urban LoRaWAN Networks. [PDF]

Sensors (Basel)
Osadchyi V, Bondarchuk A, Bondarenko O, Pyroh M, Kryvolapov H, Kryvolapov Y.   +5 more
europepmc   +1 more source

Budget-Aware Routing for Long Clinical Text. [PDF]

Find ACL ACL
Qureshi K, Martin G, Peng Y.
europepmc   +1 more source

GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT. [PDF]

Front Artif Intell
Wang Z, Yuan X, Chen J.
europepmc   +1 more source

Submodular Reinforcement Learning [PDF]

In reinforcement learning (RL), rewards of states are typically considered additive, and following the Markov assumption, they are $\textit{independent}$ of states visited previously.
Mutný, Mojmír, Prajapat, Manish, Krause, Andreas, Zeilinger, Melanie N.   +3 more
core  

Ranking with submodular functions on a budget. [PDF]

Data Min Knowl Discov, 2022
Zhang G, Tatti N, Gionis A.
europepmc   +1 more source