Learning-augmented mechanism using identity-of-max predictions for online utility maximization achieves consistency to full-info optimum and robustness to best implementable solution.
The Stochastic Matching Problem: Beating Half with a Non-Adaptive Algorithm , booktitle =
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Buyer-offering mechanisms with reserve prices achieve a 0.746-approximation to welfare in bilateral trade, surpassing fixed-price limits.
Introduces a distributed stochastic setting for graph optimization and supplies fast approximation algorithms for matching, vertex cover, and dominating set that surpass non-stochastic lower bounds.
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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Welfare Maximization in Bilateral Trade: Improved Approximation Guarantees Beyond the Fixed Price Barrier
Buyer-offering mechanisms with reserve prices achieve a 0.746-approximation to welfare in bilateral trade, surpassing fixed-price limits.
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Distributed Stochastic Graph Algorithms
Introduces a distributed stochastic setting for graph optimization and supplies fast approximation algorithms for matching, vertex cover, and dominating set that surpass non-stochastic lower bounds.
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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.