Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons
Authors: Wenbo Ren, Jia Liu, Ness Shroff
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct numerical simulations to confirm our theoretical results. |
| Researcher Affiliation | Academia | Wenbo Ren Dept. Computer Science & Engineering The Ohio State University ren.453@osu.edu Jia Liu Dept. Electrical & Computer Engineering The Ohio State University liu.1736@osu.edu Ness B. Shroff Dept. Electrical & Computer Engineering and Computer Science & Engineering The Ohio State University shroff.11@osu.edu |
| Pseudocode | Yes | Algorithm 1 Multi-wise Quick-Select(S, m, k) (MQSelect). |
| Open Source Code | Yes | The codes can be found in our Git Hub repo.6 https://github.com/Wenbo Ren/Multi-wise-Ranking.git |
| Open Datasets | No | The paper discusses "a set of n items" and for numerical results states that "all points are averaged over 100 independent trials with random true rankings." It does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes numerical simulations with varying parameters (n, k, m, δ) and using "random true rankings" for trials, but it does not provide specific training/test/validation dataset splits or mention any predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions that code is available in a GitHub repository but does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | In Figure 1 (a), we set n = 1000 and k = {1, 10, 100, 500}, and vary the value of m. In Figure 1 (b), we set n = 1000 and m = {2, 10, 100, 500}, and vary the value of k. In all figures, n = 1000 (except (f)), δ = 0.01 (if applicable), and all points are averaged over 100 independent trials with random true rankings. |