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..
Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities
Authors: Zaiwei Chen, Eric Mazumdar
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct numerical simulations to empirically verify the performance of our proposed algorithms. |
| Researcher Affiliation | Academia | Zaiwei Chen Purdue IE West Lafayette, IN 47907 EMAIL Eric Mazumdar Caltech CMS Pasadena, CA 91125 EMAIL |
| Pseudocode | Yes | Algorithm 1 Smoothed Fictitious Play (from Player 1 s perspective); Algorithm 2 Generalized Frank-Wolfe for Monotone Variational Inequalities; Algorithm 3 Stochastic Frank-Wolfe for Monotone Variational Inequalities |
| Open Source Code | No | This paper is a theoretical work, and the numerical simulations are conducted on synthetic examples to demonstrate the effectiveness of the proposed algorithm. |
| Open Datasets | No | The paper uses synthetic game setups (Rock-Paper-Scissors, Burglar-Policeman, Randomly Generated Matrix Game) for numerical simulations, but does not provide concrete access information or formal citations for these as publicly available datasets. |
| Dataset Splits | No | The numerical simulations describe game setups and parameter choices but do not specify explicit training, validation, or test splits for the data. |
| Hardware Specification | No | The paper states that it is theoretical work with numerical simulations on synthetic examples, and thus does not provide specific hardware details (like GPU/CPU models or memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the numerical simulations. |
| Experiment Setup | Yes | In the stochastic setting, we choose τ = 0.1, α = 0.1, and β = 0.01 in Algorithm 3. |