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Emulating the Expert: Inverse Optimization through Online Learning

ICML 2017 · Bandits and online learning › Online convex optimization and games · approach: Online learning with feedback graphs · 28 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
increase iterations from 10 to 100MWU inverse optimization · Integer Knapsack n=1000average objective error: 0.1511 -> 0.0185Helped
increase iterations from 100 to 1000MWU inverse optimization · Integer Knapsack n=1000average objective error: 0.0185 -> 0.0036Helped
increase iterations from 10 to 100MWU inverse optimization · Integer Knapsack n=1000average solution error: 0.0492 -> 0.0087Helped
increase iterations from 100 to 1000MWU inverse optimization · Integer Knapsack n=1000average solution error: 0.0087 -> 0.0013Helped
increase iterations from 10 to 100MWU inverse optimization · Integer Knapsack n=1000average cumulative error: 0.2003 -> 0.0272Helped
increase iterations from 100 to 1000MWU inverse optimization · Integer Knapsack n=1000average cumulative error: 0.0272 -> 0.0049Helped

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