Revenue maximization via machine learning with noisy data
Authors: Ellen Vitercik, Tom Yan
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide guarantees when arbitrarily correlated noise is added to the training set; we only require that the noise has bounded magnitude or is sub-Gaussian. Our main contribution is a sensitivity analysis of revenue with respect to the noise s magnitude. |
| Researcher Affiliation | Academia | Ellen Vitercik Department of Electrical Engineering and Computer Sciences University of California, Berkeley vitercik@berkeley.edu Tom Yan Department of Machine Learning Carnegie Mellon University tyyan@cmu.edu |
| Pseudocode | No | The paper presents mathematical definitions, theorems, lemmas, and proofs, but does not include pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links to source code, nor does it state that code is available in supplementary materials or upon request. The checklist explicitly states N/A for code for reproducing experimental results. |
| Open Datasets | No | The paper states 'A major bottleneck for empirical evaluations in learningbased mechanism design is the lack of public datasets we are only aware of one, from e Bay [44]' but does not provide access information for any dataset used for its analysis, which is primarily theoretical. |
| Dataset Splits | No | The paper is theoretical and does not report experimental results that would require data partitioning into training, validation, and test sets. Therefore, no specific dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup that would require specific software dependencies. Therefore, no software details with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not report experimental results, thus no specific experimental setup details such as hyperparameters or training configurations are provided. |