Research map / Learning theory and algorithms / Submodular and game algorithms
Minimizing a Submodular Function from Samples
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Restrict learner to polynomially many samples from any distribution for a PAC-learnable submodular family with range [0,1] | submodular minimization from samples · general distribution, unconstrained minimization | additive approximation | No clear effect |
| Return empty set or ground set with probability 1/2 each (no samples) | unconstrained submodular minimization · general submodular functions | additive approximation: achieves 1/2 additive approximation, matching the hardness bound | Helped |
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