Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg
Authors: Yi Feng, Georgios Piliouras, Xiao Wang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |