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..
Accelerated Regularized Learning in Finite N-Person Games
Authors: Kyriakos Lotidis, Angeliki Giannou, Panayotis Mertikopoulos, Nicholas Bambos
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude this section with a series of numerical simulations to validate the performance of (FTXL). |
| Researcher Affiliation | Academia | Kyriakos Lotidis Stanford University EMAIL; Angeliki Giannou University of Wisconsin Madison EMAIL; Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP LIG 38000 Grenoble, France EMAIL; Nicholas Bambos Stanford University EMAIL |
| Pseudocode | Yes | π¦π,π+1 = π¦π,π+ πΎππ,π+1; ππ,π+1 = ππ,π+ πΎΛπ£π,π; π₯π,π= ππ(π¦π,π) . (FTXL) |
| Open Source Code | Yes | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: The code is included in the supplemental material. |
| Open Datasets | No | The paper does not use a publicly available or open dataset in the traditional sense for training. Instead, it defines the parameters of specific game types (zero-sum game, congestion game) for its numerical simulations. No concrete access information like a link, DOI, or citation to a pre-existing dataset is provided. |
| Dataset Splits | No | The paper defines specific game parameters for its simulations rather than using external datasets with explicit train/validation/test splits. It conducts '100 separate trials' but does not define a separate validation split within these trials. |
| Hardware Specification | Yes | The experiments have been implemented using Python 3.11.5 on a M1 Mac Book Air with 16GB of RAM. |
| Software Dependencies | Yes | The experiments have been implemented using Python 3.11.5 on a M1 Mac Book Air with 16GB of RAM. |
| Experiment Setup | Yes | For the zero-sum game, all runs were initialized with π¦1 = 0, and we used constant step-size πΎ= 10 2, and exploration parameter π= 10 1, where applicable. For the congestion game, the initial state π¦1 for each run was drawn uniformly at random in [ 1, 1]2, and we used constant step-size πΎ= 10 2, and exploration parameter ππ= 1/π1/4, where applicable. |