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..
Estimating $α$-Rank from A Few Entries with Low Rank Matrix Completion
Authors: Yali Du, Xue Yan, Xu Chen, Jun Wang, Haifeng Zhang
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on evaluating the strategies in three synthetic games and twelve real world games demonstrate that strategy evaluation from a few entries can lead to comparable performance to algorithms with full knowledge of the payoff matrix. |
| Researcher Affiliation | Academia | 1 University College London, UK 2 Institute of Automation, Chinese Academy of Sciences 3Beijing Key Laboratory of Big Data Management and Analysis Methods, GSAI, Renmin University of China. |
| Pseudocode | Yes | Algorithm 1 gives the details of Opt Eval-1. Details of Opt Space algo-rithm are in Appendix A. Algorithm 2 gives the details of Opt Eval-2. |
| Open Source Code | Yes | The demo and code for this project are released under https://github.com/yalidu/optEval.git. |
| Open Datasets | Yes | Gaussian games (Rashid et al., 2021). Bernoulli games (Rowland et al., 2019). Soccer meta-game (Liu et al., 2018). Real world games (Czarnecki et al., 2020) |
| Dataset Splits | No | The paper describes the datasets used (Gaussian, Bernoulli, Soccer meta-game, Real world games) but does not provide explicit training, validation, or test split percentages, sample counts, or references to predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We evaluate all methods on the finiteregime with = 0.001. Four metrics are considered to evaluate both the correctness of the recovered matrix and the task performance. ... δ is the confidence level on the estimation of payoffs and is set to 0.01. ... To obtain empirical payoffs c Mij, 8(i, j) 2 , agent i and j need to compete against each other for a sufficient number of times. ... we run Opt Eval with a chosen rank r = 5 |