Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Dynamic pricing and assortment under a contextual MNL demand
Authors: Noemie Perivier, Vineet Goyal
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,796 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,230 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3. 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)? [Yes] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] |
| Researcher Affiliation | Academia | Vineet Goyal Columbia University New York, NY EMAIL Noemie Perivier Columbia University New York, NY EMAIL |
| Pseudocode | Yes | Algorithm 1 Online Newton method for multiple product pricing. Algorithm 2 OFU-MNL. |
| Open Source Code | No | The main body of the paper does not contain an explicit statement or URL indicating the release of source code for the described methodology. While the checklist indicates 'Yes' for including code, it does not provide the concrete access details within the paper's main text. |
| Open Datasets | No | The paper does not explicitly state which public datasets were used for experiments, nor does it provide links or citations for dataset access. It mentions |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. While the checklist indicates that training details were specified, these are not present in the main text. |
| Hardware Specification | No | The paper does not specify any hardware used for running experiments or numerical comparisons. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software, libraries, or solvers used in the research or experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, model initialization, or training schedules. While the checklist indicates that training details were specified, these are not present in the main text. |