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..
Refinement Methods for Distributed Distribution Estimation under $\ell^p$-Losses
Authors: Deheng Yuan, Tao Guo, Zhongyi Huang
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [NA] Justification: The paper does not contain experiments. |
| Researcher Affiliation | Academia | Deheng Yuan1, Tao Guo2,3, Zhongyi Huang1 1Department of Mathematical Sciences, Tsinghua University 2School of Cyber Science and Engineering, Southeast University 3State Key Laboratory of Integrated Services Networks, Xidian University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Successive Refinement Subroutine SSRSub(m , n, k, l, l0, p) ... Algorithm 2 Non-Interactive Successive Refinement Subroutine SSRSub(m , n, k, l, l0, 1) |
| Open Source Code | No | 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: [NA] Justification: The paper does not include experiments. ... Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets? Answer: [NA] Justification: The paper does not release new assets. |
| Open Datasets | No | 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: [NA] Justification: The paper does not include experiments. |
| Dataset Splits | No | Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments? Answer: [NA] Justification: The paper does not include experiments. |
| 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: [NA] Justification: The paper does not include experiments. |
| Software Dependencies | No | 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: [NA] Justification: The paper does not include experiments. |
| Experiment Setup | No | Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] Justification: The paper does not include experiments. |