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 [1].
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. |