Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers
Authors: Yuan Deng, Vahab Mirrokni, Hanrui Zhang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 dengyuan@google.com Vahab Mirrokni Google Research mirrokni@google.com Hanrui Zhang Carnegie Mellon University hanruiz1@cs.cmu.edu |
| 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. |