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A One-Size-Fits-All Approach to Improving Randomness in Paper Assignment

NeurIPS 2023 · Learning theory and algorithms › Prediction and decision losses · approach: Adaptive testing and experiments · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · AAMAS 2015average maximum probability: 0.80 -> 0.74Helped
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · AAMAS 2015support size: 2501 -> 28108Helped
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · AAMAS 2015entropy: 531.40 -> 1953.55Helped
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · AAMAS 2015L2 norm: 37.33 -> 32.33Helped
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · AAMAS 2015maximum assignment probability: 0.80 -> 0.80No clear effect
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · ICLR 2018maximum assignment probability: 0.90 -> 0.92Hurt
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · ICLR 2018average maximum probability: 0.90 -> 0.87Helped
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · ICLR 2018support size: 3648 -> 7122Helped
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · ICLR 2018entropy: 595.25 -> 1589.95Helped
Use concave perturbed quality instead of linear quality maximizationPLRA paper assignment · ICLR 2018L2 norm: 47.83 -> 42.49Helped
Use quadratic rather than exponential strictly concave perturbation (ablation)Perturbed Maximization · AAMAS 2015 and ICLR 2018randomness metricsNo clear effect
Use the network-flow approximation rather than Gurobi optimization (ablation)PM-Q and PM-E · AAMAS 2015support size: 28108 -> 5849Hurt

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