Anytime-Valid Inference For Multinomial Count Data
Authors: Michael Lindon, Alan Malek
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide practical solutions through sequential tests of multinomial hypotheses... which we illustrate with several industry applications. We include two real-world case studies of how this methodology is employed with practical value at a leading internet streaming company. We have several simulations in the appendix, which all include clear instructions on how they were created. |
| Researcher Affiliation | Industry | Michael Lindon Netflix michael.s.lindon@gmail.com Alan Malek alan.malek@gmail.com |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | The paper states, 'We have several simulations in the appendix, which all include clear instructions on how they were created', but it does not provide an explicit statement about releasing the source code for the methodology or a specific URL to a code repository. |
| Open Datasets | No | The paper refers to 'a signup funnel experiment at Netflix' and 'a canary test Netflix', indicating the use of internal company data, but does not provide any concrete access information (link, DOI, repository, or citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information, such as exact percentages or sample counts for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, such as concrete hyperparameter values or training configurations, in the main text. |