Proposes single-stage and two-stage distributed implicit zeroth-order gradient tracking methods for stochastic MPECs over networks that achieve best-known complexity bounds for centralized nonsmooth nonconvex stochastic optimization under uniqueness and Lipschitz assumptions.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
Fair-SMW uses SMW identity and alternative Laplacians to produce group-fair spectral clustering that is twice as fast and twice as balanced as prior methods on SBM and real network data.
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On the Resolution of Stochastic MPECs over Networks: Distributed Implicit Zeroth-Order Gradient Tracking Methods
Proposes single-stage and two-stage distributed implicit zeroth-order gradient tracking methods for stochastic MPECs over networks that achieve best-known complexity bounds for centralized nonsmooth nonconvex stochastic optimization under uniqueness and Lipschitz assumptions.
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Alternatives to the Laplacian for Scalable Spectral Clustering with Group Fairness Constraints
Fair-SMW uses SMW identity and alternative Laplacians to produce group-fair spectral clustering that is twice as fast and twice as balanced as prior methods on SBM and real network data.