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..
Active Seriation: Efficient Ordering Recovery with Statistical Guarantees
Authors: James Cheshire, Yann Issartel
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Empirical results We illustrate the behavior of ASII through numerical experiments and a real-data example. Numerical simulations. We assess the empirical behavior of ASII on synthetic data and compare it to three benchmark methods... Figure 3 reports the empirical probability of error of all methods... Application to real data. We further assess the robustness of ASII on real single-cell RNA sequencing data (human primordial germ cells... Figure 4: Similarity matrix of a single-cell RNA-seq dataset before and after reordering by ASII. |
| Researcher Affiliation | Academia | James Cheshire Yann Issartel LTCI, Télécom Paris, Institut Polytechnique de Paris |
| Pseudocode | Yes | The pseudocode of TEST is provided in Appendix B. A pseudocode of this procedure is given in Appendix B. |
| Open Source Code | Yes | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: All code used to produce empirical results is provided |
| Open Datasets | Yes | Application to real data. We further assess the robustness of ASII on real single-cell RNA sequencing data (human primordial germ cells, from [Guo et al., 2015], previously analyzed by [Cai and Ma, 2023]). |
| Dataset Splits | No | Numerical simulations. We assess the empirical behavior of ASII on synthetic data... Each experiment uses n = 10 items and T = 10,000 observations. For each value of , 100 Monte Carlo runs are split into 10 equal groups; error bars show the 0.1 and 0.9 quantiles of the empirical error across groups. The paper describes a Monte Carlo evaluation for statistical significance, not a split of a dataset into training/validation/testing subsets. No explicit dataset splits are provided for either synthetic or real data. |
| Hardware Specification | No | Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [No] Justification: Experiments are purely illustrative as this is a theoretically focused work |
| Software Dependencies | No | While the paper states that code is provided, it does not explicitly list specific software dependencies with version numbers in the main text or the NeurIPS checklist. |
| Experiment Setup | Yes | Numerical simulations. ... Each experiment uses n = 10 items and T = 10,000 observations. ... Each call to the subroutine TEST is performed with a limited sampling budget, typically of order O(T/( n log2 k))... BBS uses a small number of samples per TEST, of order O(T/( n log2 k)), just enough to ensure a constant success probability (e.g., around 3/4). |