Research map / Learning theory and algorithms / Submodular and game algorithms
Online Non-Monotone DR-Submodular Maximization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the Meta-Frank-Wolfe construction with online vee-learning oracles over down-closed sets | Online non-monotone DR-submodular maximization · Down-closed convex sets | approximation ratio: 1/e | Helped |
| Lift a lattice discretization to a higher-dimensional discrete submodular problem | Online non-monotone DR-submodular maximization · Hypercube [0,1]^n | approximation ratio: 1/2 | Helped |
| Adapt Meta-Frank-Wolfe step sizes for a general convex domain | Online non-monotone DR-submodular maximization · General convex sets | approximation ratio: \left( \frac{1- \min_{\vect{x} \in \mathcal{K}} \|\vect{x}\|_{\infty} }{3\sqrt{3}} \right) | Helped |
| Initialize at the origin when the domain contains it | Meta-Frank-Wolfe · General convex sets containing the origin | approximation ratio: 1/(3√3) | Helped |
Closest papers by meaning
- Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets (ICML 2026 · similarity 0.94)
- Online Submodular Maximization via Online Convex Optimization (AAAI 2024 · similarity 0.93)
- Non-Monotone DR-Submodular Function Maximization (AAAI 2017 · similarity 0.93)
- Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints (NeurIPS 2024 · similarity 0.92)
- Online Two-Stage Submodular Maximization (NeurIPS 2025 · similarity 0.92)
- From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization (NeurIPS 2024 · similarity 0.91)
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