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..
Asynchronous Accelerated Stochastic Gradient Descent
Authors: Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma, Tie-Yan Liu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we tested AASGD on a few benchmark datasets. The experimental results verified our theoretical findings and indicated that AASGD could be a highly effective and efficient algorithm for practical use. |
| Researcher Affiliation | Collaboration | 1 School of Mathematical Sciences, Peking University, EMAIL 2Microsoft Research, EMAIL 3Fudan University, Jingcheng EMAIL 4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, EMAIL |
| Pseudocode | Yes | Algorithm 1 Asynchronous Accelerated SGD (AASGD) |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We conducted binary classification tasks on three benchmark dataset: rcv1, real-sim, news20... The detailed information about the three datasets can be found from Lib SVM website. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It only mentions the use of 'training error' as a stopping criterion. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not mention any software dependencies with specific version numbers. |
| Experiment Setup | Yes | In the AASGD algorithm, we set the number of block partitions as m = d/100, the mini-batch size as pn/ P (P is the number of threads), and the inner loop K = 2mn. The stopping criterion in our experiments is the training error smaller than 10 10 (i.e., F(xk) F(x ) < 10 10). For the datasets we used, Lmax = Lres < 0.25 since the input data is normalized [Reddi et al., 2015], P, µ = 1/pn = 0.01 [Shamir et al., 2014]. In SASGD and AASGD, we set stepsizes 0 = 0.2 and = 0.1/P, which satisfy our assumptions in the theorems and corollaries. |