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..
Quasi-Newton Methods for Saddle Point Problems
Authors: Chengchang Liu, Luo Luo
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments show proposed algorithms outperform classical first-order methods. and Section 5 Numerical Experiments with subsections like AUC Maximization and Adversarial Debiasing and Figure 1 and 2 showing iteration numbers vs. g(z) 2 and CPU time (second) vs. g(z) 2. |
| Researcher Affiliation | Academia | Chengchang Liu Department of Computer Science & Engineering The Chinese University of Hong Kong EMAIL Luo Luo School of Data Science Fudan University EMAIL |
| Pseudocode | Yes | Algorithm 1 Fast-Chol(H, L, u), Algorithm 2 Random-Broyden-Quadratic, Algorithm 3 Random-BFGS-Quadratic, Algorithm 4 Random-SR1-Quadratic, Algorithm 5 Random-Broyden-General, Algorithm 6 Random-BFGS-General, Algorithm 7 Random-SR1-General |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] We present them in the supplemental material. |
| Open Datasets | Yes | We evaluate all algorithms on three real-world datasets a9a , w8a and sido0 . and fairness-aware binary classification dataset adult , bank market and law school [30]. |
| Dataset Splits | No | The provided text mentions using datasets but does not explicitly detail training, validation, and test splits with percentages or specific methods for partitioning the data. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix E. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as libraries or programming languages used in the experiments. |
| Experiment Setup | Yes | We set λ = 100/n (for AUC Maximization) and We set the parameters β, λ and γ as 0.5, 10 4 and 10 4 respectively. The dimension of the problem is d = m + 1. Since the objective function is non-quadratic, we conduct the proposed algorithms in Section 3.3 (Algorithm 5, 6 and 7) here. We use extragradient as warm up to achieve the local condition for proposed algorithms. (for Adversarial Debiasing). |