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..
A Catalyst Framework for Minimax Optimization
Authors: Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carry out several numerical experiments showcasing the superiority of the Catalyst framework in practice. |
| Researcher Affiliation | Academia | Junchi Yang UIUC EMAIL Siqi Zhang UIUC EMAIL Negar Kiyavash EPFL EMAIL Niao He UIUC & ETH Zurich EMAIL |
| Pseudocode | Yes | Algorithm 1 Catalyst for SC-C Minimax Optimization |
| Open Source Code | No | No explicit statement or link providing access to the authors' source code for the methodology described in the paper. |
| Open Datasets | No | We generate two datasets with (1) β = 1 and σ0 R1000 uniformly from [0, 100]1000, (2) β = 1 and σ0 R500 uniformly from [0, 10]500. |
| Dataset Splits | No | The paper describes generating datasets but does not provide specific details on training, validation, or test splits, or reference any standard predefined splits. |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU types, or cloud instance specifications) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library or solver names with version numbers) are mentioned in the paper. |
| Experiment Setup | Yes | In Figure 1, we apply the same stepsizes to EG and subroutine in Catalyst-EG, and we compare their convergence results with stepsizes from small to large. In Figure 2, we compare four algorithms: extragradient (EG), SVRG, Catalyst-EG, Catalyst-SVRG with besttuned stepsizes... In Catalyst, we use xt PX (xt β xf(xt, yt)) /β + yt PY(yt + β yf(xt, yt)) /β as stopping criterion for subproblem, which is discussed in Section 2. We control the subroutine accuracy ϵ(t) as max{c/t8, ϵ}, where c is a constant and ϵ is a prefixed threshold. |