Near-Optimal No-Regret Learning in General Games
Authors: Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [N/A] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A] (c) Did you report error bars (e.g., with respect to the random seed after running experi- ments multiple times)? [N/A] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Researcher Affiliation | Academia | Constantinos Daskalakis MIT CSAIL costis@csail.mit.edu Maxwell Fishelson MIT CSAIL maxfish@mit.edu Noah Golowich MIT CSAIL nzg@csail.mit.edu |
| Pseudocode | No | The Optimistic Hedge algorithm is described using mathematical equations (Equation 1) in Section 2, rather than a formal pseudocode block or an explicitly labeled algorithm section. |
| Open Source Code | No | The authors indicate 'N/A' for questions regarding code availability under the 'If you ran experiments' section, and no explicit statement or link for open-source code for their methodology is provided in the paper. |
| Open Datasets | No | This is a theoretical paper that does not involve experimental training on datasets; the authors marked 'N/A' for all experiment-related questions. |
| Dataset Splits | No | This is a theoretical paper and does not discuss training/validation/test dataset splits; the authors marked 'N/A' for all experiment-related questions. |
| Hardware Specification | No | This is a theoretical paper and does not mention any specific hardware used for running experiments. The authors marked 'N/A' for all experiment-related questions. |
| Software Dependencies | No | This is a theoretical paper and does not list any specific software dependencies with version numbers for experimental reproducibility. The authors marked 'N/A' for all experiment-related questions. |
| Experiment Setup | No | This is a theoretical paper and does not provide details on experimental setup, hyperparameters, or training configurations. The authors marked 'N/A' for all experiment-related questions. |