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..
Maximum Selection and Ranking under Noisy Comparisons
Authors: Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the performance of our algorithms with that of others over simulated data. Similar to (Yue & Joachims, 2011), we consider the stochastic model where p(i, j) = 0.6 8i < j. Note that this model satisfies both SST and STI. We find 0.05-maximum with error probability δ = 0.1. Observe that i = 1 is the only 0.05-maximum. We compare the sample complexity of KNOCKOUT with that of BTMPAC (Yue & Joachims, 2011), Mallows MPI (Busa-Fekete et al., 2014a), and AR (Heckel et al., 2016). |
| Researcher Affiliation | Collaboration | 1University of California, San Diego 2Google Research. |
| Pseudocode | Yes | Algorithm 1 COMPRARE Input: element i, element j, bias , confidence δ. Initialize: ˆpi = 1 2, m = 1 2 2 log 2 δ , r = 0, wi = 0. 1. while (| ˆpi 1 2| ˆc and r m) (a) Compare i and j. if i wins wi = wi + 1. (b) r = r + 1, ˆpi = wi r . 2. ˆc = 1 2r log 4r2 δ . If wi r < 1 2 Output: j. else Output: i. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of open-source code for the methodology described, nor does it include a link to a code repository. |
| Open Datasets | No | The paper conducts experiments on 'simulated data' and describes the stochastic models used (e.g., 'p(i, j) = 0.6' or 'Mallows model'), but it does not refer to any publicly available dataset with a link, DOI, or formal citation. |
| Dataset Splits | No | The paper conducts simulations on various stochastic models, but it does not specify any training, validation, or test dataset splits, as it's not a typical machine learning task with pre-partitioned datasets. |
| Hardware Specification | No | The paper does not mention any specific hardware used for running the simulations or experiments. |
| Software Dependencies | No | The paper does not provide details about specific software dependencies or their version numbers used for implementation or experimentation. |
| Experiment Setup | Yes | Similar to (Yue & Joachims, 2011), we consider the stochastic model where p(i, j) = 0.6 8i < j. Note that this model satisfies both SST and STI. We find 0.05-maximum with error probability δ = 0.1. ... As in (Busa-Fekete et al., 2014a), we consider n = 10 elements and various values for φ: 0.03, 0.1, 0.3, 0.5, 0.7, 0.8, 0.9, 0.95 and 0.99. |