Research map / Learning theory and algorithms / Combinatorial and search optimization
Dynamic Programming for Predict+Optimise
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| optimise regret directly with DP piecewise-linear coordinate descent instead of ridge regression | linear predictor for knapsack · Energy knapsack capacity 10% test | average test regret: 94 -> 89 | Helped |
| optimise regret directly with DP+PLF instead of ridge regression | linear predictor for knapsack · Energy knapsack capacity 30% test | average test regret: 147 -> 141 | Helped |
| optimise regret directly with DP+PLF instead of ridge regression | linear predictor for knapsack · Energy knapsack capacity 50% test | average test regret: 109 -> 112 | Hurt |
| DP+PLF vs SPO convex surrogate | linear predictor for knapsack · Energy knapsack capacity 30% test | average test regret: 223 -> 141 | Helped |
| DP+PLF vs QPTL relaxation | linear predictor for knapsack · Energy knapsack capacity 10% test | average test regret: 142 -> 89 | Helped |
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