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Learning to Clear the Market

ICML 2019 · Learning theory and algorithms › Submodular and game algorithms · approach: Learned auction design · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the clearing loss instead of the benchmark pricing lossesLinear pricing model · Google Ad Exchange test setrevenue at a fixed match rateHelped
Use the clearing loss instead of the benchmark pricing lossesLinear pricing model · Google Ad Exchange test setrevenue at a fixed social welfare: over 10% revenue improvement with less than 2% impact on buyer welfareHelped
Set the clearing-loss supply parameter to λ = 0.25 (ablation)Clearing loss · Google Ad Exchange test setrevenue improvement: 20%Helped
Set the clearing-loss supply parameter to λ = 0.25 (ablation)Clearing loss · Google Ad Exchange test setmatch rate: 30%Hurt
Vary λ to control supply and price aggressiveness (ablation)Clearing loss · Google Ad Exchange test setmatch rateHelped
Use the default clearing-loss parameter λ = 1 (ablation)Clearing loss · Google Ad Exchange test setmatch rate: 1 - 1/e ≈ 0.63Mixed
Use the stated match-rate formula to choose λ (ablation)Clearing loss · Google Ad Exchange test setmatch rate: λ = log(1/(1-{sf MR}))Helped
Fit prices by least-squares regression on the highest bid rather than clearing lossLinear pricing model · Google Ad Exchange test setsocial welfare: almost 5%Hurt
Use revenue surrogate loss instead of clearing lossLinear pricing model · Google Ad Exchange test setconvergence speed: 400K iterationsHurt
Vary γ across {0.25, 0.5, 0.75, 1} and select γ = 0.75 (ablation)Revenue surrogate · Google Ad Exchange test setrevenueHelped
Use least-squares regression on the highest bid without regularizationLinear pricing model · Google Ad Exchange test setrevenueHelped
Use least-squares regression on the highest bid without regularizationLinear pricing model · Google Ad Exchange test setmatch rateHurt

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