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..
Near-Optimal No-Regret Learning in General Games
Authors: Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich
NeurIPS 2021 | Venue PDF | 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 EMAIL Maxwell Fishelson MIT CSAIL EMAIL Noah Golowich MIT CSAIL EMAIL |
| 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. |