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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]

Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers

Authors: Yuan Deng, Vahab Mirrokni, Hanrui Zhang

NeurIPS 2022 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical The proof of the theorem, as well as all other missing proofs, is deferred to the appendix. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]
Researcher Affiliation Collaboration Yuan Deng Google Research EMAIL Vahab Mirrokni Google Research EMAIL Hanrui Zhang Carnegie Mellon University EMAIL
Pseudocode No No structured pseudocode or algorithm blocks were found in the paper.
Open Source Code No If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]
Open Datasets No The paper is theoretical and does not involve empirical experiments with datasets; thus, no information regarding publicly available training data is provided.
Dataset Splits No The paper is theoretical and does not involve empirical experiments with datasets; thus, no information regarding dataset splits for validation is provided.
Hardware Specification No The paper is theoretical and does not report on computational experiments that would require specific hardware specifications for reproducibility.
Software Dependencies No The paper is theoretical and does not specify software dependencies with version numbers for reproducibility.
Experiment Setup No The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations.