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..
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates
Authors: Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The numerical experiments show the proposed methods significantly outperform the baseline methods. ... We compare the proposed methods LISR-1 and LISR-k with baseline methods including IQN (Mokhtari, Eisen, and Ribeiro 2018) and SLIQN (Lahoti et al. 2023). We test all methods on the problems of quadratic programming and regularized logistic regression. |
| Researcher Affiliation | Collaboration | Zhuanghua Liu1, 2, Luo Luo*3, Bryan Kian Hsiang Low1 1Department of Computer Science, National University of Singapore 2CNRS@CREATE LTD, 1 Create Way, #08-01 CREATE Tower, Singapore 138602 3School of Data Science, Fudan University |
| Pseudocode | Yes | Algorithm 1: LISR-1 |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | We conduct our experiments on nine real-world datasets ( a9a , w8a , ijcnn , mushrooms , phishing , svmguide3 , german.numer , splice and covtype ) from LIBSVM repository. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For the LISR-k method, we set k = 5 for all of the cases. For the fairness of comparison, we run all algorithms from the same initial point. ... We run the experiments by taking n = 1000, d = 50 and ΞΎ {4, 8, 12}... We take Ξ» = 10 3 for a9a , mushrooms , svmguide3 , german.numer , covtype and Ξ» = 10 4 for others. |