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..
Efficient Projection-free Algorithms for Saddle Point Problems
Authors: Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the effectiveness of our algorithms and verify our theoretical guarantees. We also conduct experiments on several real-world data sets for robust optimization problem to validate our theoretical analysis. The empirical results show that the proposed methods outperform previous projection-free and projection-based methods when the feasible set is complicated. In this section, we empirically evaluate the performance of our methods on the robust multiclass classification problem introduced in Section 2.3. |
| Researcher Affiliation | Academia | Cheng Chen1 Luo Luo2 Weinan Zhang1 Yong Yu1 1Shanghai Jiao Tong University 2The Hong Kong University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 CGS Method for strongly convex functions, Algorithm 2 Procedure q+=Cnd G(r, q, β, η, Ω), Algorithm 3 Mirror-Prox Conditional Gradient Sliding, Algorithm 4 Procedure (x R, y R, v R)=Prox-step(f, x0, y0, z, v, γ, α, ζ, ϵ), Algorithm 5 Inexact STORC (i STORC), Algorithm 6 Mirror-Prox Stochastic Conditional Gradient Sliding, Algorithm 7 Procedure (x R, y R, v R)=Stochastic-Prox-step(f, x0, y0, z, v, γ, α, ζ, P, ϵ) |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We conduct experiments on three real-world data sets from the LIBSVM repository2: rcv1 (n = 15, 564, d = 47, 236, h = 53), sector (n = 6, 412, d = 55, 197, h = 105) and news20 (n = 15, 935, d = 62, 061, h = 20). 2https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper mentions using 'real-world data sets' but does not specify the train/validation/test splits, percentages, or methodology for data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We implement the mini-batch version of SVRE with batch size 100. The learning rate of SVRE is searched in {10 1, 10 2, . . . , 10 6}. On the other hand, the parameters of projection-free methods follows what the theory suggests. |