Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Prior-independent Dynamic Auctions for a Value-maximizing Buyer
Authors: Yuan Deng, Hanrui Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a novel dynamic mechanism achieving O(T 2/3) regret against a strategic automated bidder, where T is the time horizon. Our dynamic mechanism adopts the explore-and-exploit scheme by ο¬rst estimating the bidder s value distribution. We then offer a robust version of the single-stage revenue-optimal mechanism to extract the revenue. A cornerstone of our dynamic mechanism in the exploration phase is a novel prior-independent single-stage mechanism that is incentive-compatible for automated bidders. This mechanism forces the bidder to make a trade-off between her future gain from misreporting her values and her immediate loss, leading to a Wasserstein distance bound on the magnitude of misreporting from the automated bidder which is precisely the form of error that our exploitation mechanisms are robust against. |
| Researcher Affiliation | Collaboration | Yuan Deng Google Research EMAIL Hanrui Zhang Duke University EMAIL |
| Pseudocode | Yes | Figure 1: A prior-indepentent no-regret mechanism for a value-maximizing buyer. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any open-source code for the described methodology. |
| Open Datasets | No | The paper does not use a publicly available or open dataset. It defines a theoretical 'value distribution D ([0, 1])' for its analysis. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with training, validation, and test splits. |
| Hardware Specification | No | The paper describes a theoretical mechanism and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper describes a theoretical mechanism and does not mention any software dependencies with version numbers. |
| Experiment Setup | No | The paper describes a theoretical mechanism and does not provide details about an experimental setup, hyperparameters, or training settings. |