Research map / Learning theory and algorithms / Submodular and game algorithms
Learning to Optimize Combinatorial Functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learned coverage-function model (DOPS) instead of OPS, PMAC, LinReg, and Slope baselines for choosing trending hashtags | choosing trending items on Twitter · Twitter hashtags | avg. num. adopters | Helped |
| Increasing test set size m | baselines (OPS, PMAC, LinReg, Slope) · Twitter hashtags | avg. num. adopters | Hurt |
| Increasing test set size m | DOPS · Twitter hashtags | avg. num. adopters | Helped |
Closest papers by meaning
- Minimizing a Submodular Function from Samples (NeurIPS 2017 · similarity 0.91)
- Randomized Algorithms for Submodular Function Maximization with a $k$-System Constraint (ICML 2021 · similarity 0.89)
- Non-Monotone DR-Submodular Function Maximization (AAAI 2017 · similarity 0.88)
- Online Two-Stage Submodular Maximization (NeurIPS 2025 · similarity 0.88)
- Breaking Barriers: Combinatorial Algorithms for Non-Monotone Submodular Maximization with Sublinear Adaptivity and $1/e$ Approximation (ICML 2025 · similarity 0.87)
- Consistent Submodular Maximization (ICML 2024 · similarity 0.87)
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