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..
Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg
Authors: Yi Feng, Georgios Piliouras, Xiao Wang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we provide numerical experiments illustrating the covariance evolution results proved for Euclidean norm regularized FTRL in Theorem 5.1. |
| Researcher Affiliation | Collaboration | 1Shanghai University of Finance and Economics, Shanghai, China 2Google Deep Mind, London, United Kingdom 3Key Laboratory of Interdisciplinary Research of Computation and Economics, China. |
| Pseudocode | No | The paper presents mathematical formulations and derivations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide explicit statements about the release of open-source code for the described methodology or links to code repositories. |
| Open Datasets | No | The experiments are conducted using 'randomly generated initial conditions' and 'randomly generated game', implying synthetic data rather than a specific public dataset with access information. |
| Dataset Splits | No | The paper describes numerical experiments based on initial conditions, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes numerical experiments but does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper describes numerical experiments but does not list specific software dependencies with version numbers used for these experiments. |
| Experiment Setup | No | The paper discusses the 'step size η' and 'randomly generated initial conditions' for numerical experiments, but it does not provide specific hyperparameter values, model initialization details, or other system-level training settings in the main text. |