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].
Projection-Free Methods for Solving Nonconvex-Concave Saddle Point Problems
Authors: Morteza Boroun, Erfan Yazdandoost Hamedani, Afrooz Jalilzadeh
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we implement our methods to solve Robust Multiclass Classification problem described in Example 1 and Dictionary Learning problem in Example 2. As shown in Figure 1, our algorithms outperform the competing approaches |
| Researcher Affiliation | Academia | Morteza Boroun , Erfan Yazdandoost Hamedani , Afrooz Jalilzadeh Department of Systems & Industrial Engineering The University of Arizona Tucson, AZ 85721 EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Regularized Primal-dual Conditional Gradient (R-PDCG) method Algorithm 2 Conditional Gradient with Regularized Projected Gradient Ascent (CG-RPGA) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it state that the code is released or available in supplementary materials. |
| Open Datasets | Yes | We conduct experiments on rcv1 dataset (n = 15564, d = 47236, k = 53) and news20 dataset (n = 15935, d = 62061, k = 20) from LIBSVM repository1. 1https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets |
| Dataset Splits | No | The paper mentions the datasets used (rcv1 and news20 from LIBSVM repository) but does not provide specific details on how the data was split into training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using datasets from the LIBSVM repository but does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | For all the algorithms, the step-sizes are selected as suggested by their theoretical result and scaled to have the best performance. In particular, for R-PDCG we let ฯ = 10 K5/6 and ยต = 10 3 K1/6 ; for CG-RPGA we let ฯ = 10 K3/4 and K1/4 ; for AGP we let the primal step-size 1 k, dual step-size as 0.2, and the dual regularization parameter as 10 1 k1/4 ; for SPFW both primal and dual step-sizes are selected to be diminishing as 2 k+2. |