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Optimal Auctions through Deep Learning

ICML 2019 · Learning theory and algorithms › Submodular and game algorithms · approach: Learned auction design · 90 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
compare learned auction to analytical optimum, additive single bidderRegretNet · (I)revenue: 0.550 -> 0.554No clear effect
compare to analytical optimum, unit-demand single bidderRegretNet · (II)revenue: 2.137 -> 2.137No clear effect
replace VVCA/AMAbsym auctions with learned RegretNetRegretNet · (III)revenue: 0.862 -> 0.878Helped
replace VVCA/AMAbsym with RegretNetRegretNet · (IV)revenue: 2.765 -> 2.871Helped
replace VVCA/AMAbsym with RegretNetRegretNet · (V)revenue: 4.209 -> 4.270Helped
replace item-wise Myerson with RegretNet at scaleRegretNet · (VI) 3 bidders 10 itemsrevenue: 5.310 -> 5.541Helped
replace item-wise Myerson with RegretNet at scaleRegretNet · (VII) 5 bidders 10 itemsrevenue: 6.716 -> 6.778Helped
replace bundled Myerson with RegretNetRegretNet · (VII)revenue: 5.453 -> 6.778Helped
replace LP (5 bins) with RegretNet on regretRegretNet · 2x3 additiveregret: 0.019 -> < 0.001Helped
replace LP with RegretNet on run-timeRegretNet · 2x3 additiverun-time: 69 hrs -> ∼9 hrsHelped
replace LP with RegretNet on revenueRegretNet · 2x3 additiverevenue: 1.53 -> 1.291Hurt
vary architecture depth/width (more layers or nodes) (ablation)RegretNet · held-out validationrevenueNo clear effect

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