Research map / Bandits and online learning / Online convex optimization and games
Generalized Inverse Optimization through Online Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use warm-start initialization instead of zero-vector cold-start (ablation) | Online implicit learning for consumer utility estimation · Consumer behavior: utility learning | early-round parameter estimation error | Helped |
| Use warm-start initialization instead of zero-vector cold-start (ablation) | Online implicit learning for consumer utility estimation · Consumer behavior: utility learning | long-run parameter estimation | No clear effect |
| Process observations online rather than using batch inverse optimization | Inverse optimization · Consumer behavior: utility learning | computational time | Helped |
| Learn a one-dimensional budget parameter rather than a ten-dimensional utility vector | Online implicit learning · Consumer behavior | learning speed | Helped |
| Use online implicit updates on sequential noisy observations | Online inverse optimization · Consumer behavior: utility learning | average cumulative loss: 0.2083 | Helped |
| Use online implicit updates on sequential noisy observations | Online inverse optimization · Transshipment: transportation cost learning | average cumulative loss: 0.1667 | Helped |
| Use online implicit updates instead of batch inverse optimization | Inverse optimization · Consumer behavior and transshipment | computational efficacy | Helped |
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