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..
Revenue maximization via machine learning with noisy data
Authors: Ellen Vitercik, Tom Yan
NeurIPS 2021 | Venue PDF | 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 EMAIL Tom Yan Department of Machine Learning Carnegie Mellon University EMAIL |
| 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. |