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..
Probably Approximately Precision and Recall Learning
Authors: Lee Cohen, Yishay Mansour, Shay Moran, Han Shao
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our abstract and introduction provide a summary of contributions that accurately reflect the new model and the results of this paper. This is a theoretical paper. All results are carefully proven and do not require experiments to establish correctness. |
| Researcher Affiliation | Collaboration | Lee Cohen Stanford EMAIL Yishay Mansour Tel Aviv University and Google Research EMAIL Shay Moran Technion and Google Research EMAIL Han Shao University of Maryland, College Park EMAIL |
| Pseudocode | Yes | Algorithm 1: Maximum Likelihood. One of our proposed algorithms is based on the natural idea of maximum likelihood. [...] Algorithm (realizable case): Let ε denote the desired error. Output a hypothesis goutput H such that [...] Algorithm (agnostic case): output a hypothesis goutput H such that d H(bg, goutput) = min g H d H(bg, g). |
| 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 is a theoretical paper. All results are carefully proven and do not require experiments to establish correctness. |
| 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 is a theoretical paper. All results are carefully proven and do not require experiments to establish correctness. |
| Dataset Splits | 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 is a theoretical paper. All results are carefully proven and do not require experiments to establish correctness. |
| 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 is a theoretical paper. All results are carefully proven and do not require experiments to establish correctness. |
| Software Dependencies | 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 is a theoretical paper. All results are carefully proven and do not require experiments to establish correctness. |
| 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 is a theoretical paper. All results are carefully proven and do not require experiments to establish correctness. |