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..
A KL-LUCB algorithm for Large-Scale Crowdsourcing
Authors: Ervin Tanczos, Robert Nowak, Bob Mankoff
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical results with numerical experiments based on the New Yorker Cartoon Caption Contest. and Section 5 provides experimental support for the lil-KLUCB algorithm using data from the New Yorker Caption Contest. |
| Researcher Affiliation | Collaboration | Ervin Tánczos and Robert Nowak University of Wisconsin-Madison EMAIL, EMAIL Bob Mankoff Former Cartoon Editor of the New Yorker EMAIL |
| Pseudocode | Yes | 1. Initialize by sampling every arm once. 2. While LTOP(t)(TTOP(t)(t), δ/(n 1)) max i =TOP(t)Ui(Ti(t), δ) do: Sample the following two arms: TOP(t), and arg max i =TOP(t)Ui(Ti(t), δ) and update means and confidence bounds. 3. Output TOP(t) |
| Open Source Code | No | The paper states 'These data can be found at https://github.com/nextml/caption-contest-data' which refers to data, not the open-source code for the proposed methodology. |
| Open Datasets | Yes | We corroborate our theoretical results with numerical experiments based on the New Yorker Cartoon Caption Contest. and Section 5 provides experimental support for the lil-KLUCB algorithm using data from the New Yorker Caption Contest. Footnote 7: These data can be found at https://github.com/nextml/caption-contest-data |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits (percentages, sample counts, or citations to predefined splits) for the data used in experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware details (like GPU models, CPU types, or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We set N = 8 and δ = 0.01 in our experiments. |