Research map / Learning theory and algorithms / Submodular and game algorithms
Guarantees for Greedy Maximization of Non-submodular Functions with Applications
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GREEDY instead of SDP for Bayesian A-optimality maximization | Bayesian A-optimality experimental design · Medium-scale synthetic A-optimality | runtime: roughly two orders of magnitude faster | Helped |
| Use GREEDY instead of SDP for A-optimality | Bayesian A-optimality experimental design · Medium-scale synthetic A-optimality | objective value | No clear effect |
| Use the greedy curvature alpha_G instead of total curvature alpha_total to characterize the problem | Bayesian A-optimality and LP experiments · Boston Housing and synthetic A-optimality | curvature estimate | Helped |
| Use greedy submodularity ratio gamma_G to predict performance on determinantal function maximization | Determinantal function (supermodular) maximization · Synthetic and CIFAR-10 active set selection | approximation guarantee vs performance | No clear effect |
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