Research map / Learning theory and algorithms / Submodular and game algorithms
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a time-varying distorted objective that shifts relative emphasis from cost toward utility | Distorted Greedy · Cardinality-constrained maximization | approximation guarantee: (1 - e^{-\gamma}) g(OPT) - c(OPT) | Helped |
| Sample a smaller random subset of the ground set at each iteration | Stochastic Distorted Greedy · Cardinality-constrained maximization | function evaluations: O(nk) -> O(n \log \frac{1}{\epsilon}) | Helped |
| Sample a smaller random subset of the ground set at each iteration | Stochastic Distorted Greedy · Cardinality-constrained maximization | approximation guarantee: (1 - e^{-\gamma}) -> (1 - e^{-\gamma} - \epsilon) | Hurt |
| Sample one random element per iteration instead of searching the ground set | Unconstrained Distorted Greedy · Unconstrained maximization | function evaluations: O(nk) -> O(n) | Helped |
| Use a geometric sweep to guess the unknown submodularity ratio | \gamma-Sweep · Cardinality-constrained maximization with unknown \gamma | approximation guarantee: (1 - e^{-\gamma}) -> (1 - e^{-\gamma} - O(\delta)) | Hurt |
| Combine \gamma-Sweep with Stochastic Distorted Greedy rather than Distorted Greedy | Boston Housing experimental design and Email EU directed vertex cover · Experimental applications | objective value | Helped |
Closest papers by meaning
- Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions (ICML 2018 · similarity 0.92)
- Robust Submodular Maximization: A Non-Uniform Partitioning Approach (ICML 2017 · similarity 0.92)
- Guarantees for Greedy Maximization of Non-submodular Functions with Applications (ICML 2017 · similarity 0.92)
- Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? (ICML 2018 · similarity 0.92)
- An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale (ICML 2025 · similarity 0.90)
- A Unified Approach to Submodular Maximization Under Noise (NeurIPS 2025 · similarity 0.90)
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