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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
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.
- Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming