Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
Authors: Negin Golrezaei, Adel Javanmard, Vahab Mirrokni
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The first policy called Contextual Robust Pricing (CORP) is designed for the setting where the market noise distribution is known to the seller and achieves a T-period regret of O(d log(Td) log(T)), where d is the dimension of the contextual information. The second policy, which is a variant of the first policy, is called Stable CORP (SCORP). This policy is tailored to the setting where the market noise distribution is unknown to the seller and belongs to an ambiguity set. We show that the SCORP policy has a T-period regret of O( p d log(Td) T 2/3). |
| Researcher Affiliation | Collaboration | Negin Golrezaei Sloan School of Management Massachusetts Institute of Technology Cambridge, MA golrezae@mit.edu Ad el Javanmard Data Sciences and Operations Department University of Southern California Los Angeles, CA ajavanma@usc.edu Vahab Mirrokni Google Research New York, NY mirrokni@google.com |
| Pseudocode | Yes | Table 1: CORP Policy |
| Open Source Code | No | The paper describes theoretical policies and their analysis, but does not provide any statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper that focuses on algorithm design and mathematical analysis (regret bounds) rather than empirical studies with specific datasets. Therefore, no information about public or open datasets for training is provided. |
| Dataset Splits | No | This is a theoretical paper focusing on algorithm design and mathematical analysis. It does not involve empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any implementation details that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any empirical experiments with specific experimental setup details like hyperparameters or training configurations. |