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].
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. |