Learning-augmented mechanism using identity-of-max predictions for online utility maximization achieves consistency to full-info optimum and robustness to best implementable solution.
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3 Pith papers cite this work. Polarity classification is still indexing.
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cs.GT 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Anonymous pricing guarantees 1/e of optimal revenue for mixed value and utility maximizers, improves prior bounds, and shows competition can reduce revenue.
Designs optimal and approximately optimal mechanisms for buyer utility and welfare objectives in budget-feasible procurement, including prior-free constant-factor approximations for welfare and Bayesian near-optimal mechanisms for utility.
citing papers explorer
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Knowing Who, Not How Much: Learning-Augmented Mechanisms for Consumer Utility Maximization
Learning-augmented mechanism using identity-of-max predictions for online utility maximization achieves consistency to full-info optimum and robustness to best implementable solution.
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Revenue Guarantee of Anonymous Pricing for Mixed Bidders:Bridging Value and Utility Maximizers
Anonymous pricing guarantees 1/e of optimal revenue for mixed value and utility maximizers, improves prior bounds, and shows competition can reduce revenue.
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From Welfare to Utility: Generalized Objectives in Budget-Feasible Procurement
Designs optimal and approximately optimal mechanisms for buyer utility and welfare objectives in budget-feasible procurement, including prior-free constant-factor approximations for welfare and Bayesian near-optimal mechanisms for utility.