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..
Product Distribution Learning with Imperfect Advice
Authors: Arnab Bhattacharyya, XianJun Davin Choo, Philips George John, Themis Gouleakis
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [NA] Justification: The paper is entirely theoretical. Theoretical limitations and assumptions etc are formally discussed. The claims are only made under the scope of these assumptions. |
| Researcher Affiliation | Academia | Arnab Bhattacharyya Department of Computer Science University of Warwick EMAIL Davin Choo Harvard John A. Paulson School Of Engineering And Applied Sciences Harvard University EMAIL Philips George John CNRS@CREATE & Dept. of Computer Science National University of Singapore EMAIL Themis Gouleakis College of Computing & Data Science Nanyang Technological University EMAIL |
| Pseudocode | Yes | Algorithm 1 The APPROXL1 algorithm. (...) Algorithm 2 The TESTANDOPTIMIZEMEAN algorithm. |
| 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: This paper does not include experiments, and does not provide code or data. |
| 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: This paper does not include experiments, and does not provide code or data. |
| Dataset Splits | No | 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: This 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: This paper does not include experiments. |
| Software Dependencies | No | 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: This 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: This paper does not include experiments. |