W-SparQ-BL models time-varying lower-level responses with multi-output GPs and sparse approximations to achieve sublinear dynamic regret in bilevel optimization under noise.
A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , booktitle =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Community detection is treated as hypothesis testing with test statistics and canonical-ensemble null models that maximize entropy under chosen constraints.
citing papers explorer
-
No-regret optimization of time-varying bilevel problems
W-SparQ-BL models time-varying lower-level responses with multi-output GPs and sparse approximations to achieve sublinear dynamic regret in bilevel optimization under noise.
-
Community Detection with the Canonical Ensemble
Community detection is treated as hypothesis testing with test statistics and canonical-ensemble null models that maximize entropy under chosen constraints.