Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction
Authors: Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhi-Ming Ma, Tie-Yan Liu
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted experiments on a regularized logistic regression task. The results verified our theoretical findings and demonstrated the practical efficiency of the asynchronous stochastic proximal algorithms with variance reduction. |
| Researcher Affiliation | Collaboration | Qi Meng,1 Wei Chen,2 Jingcheng Yu,3 Taifeng Wang,2 Zhi-Ming Ma,4 Tie-Yan Liu2 1 School of Mathematical Sciences, Peking University, qimeng13@pku.edu.cn 2Microsoft Research, {wche, taifengw, tie-yan.liu}@microsoft.com 3Carnegie Mellon University, jingchey@cs.cmu.edu 4Academy of Mathematics and Systems Science, Chinese Academy of Sciences, mazm@amt.ac.cn |
| Pseudocode | Yes | Algorithm 1 Async-Prox SVRG and Async-Prox SVRCD |
| Open Source Code | No | The paper does not provide an explicit statement of source code release or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We conducted binary classifications on three benchmark datasets: rcv1, real-sim, news20 (Reddi et al. 2015) |
| Dataset Splits | No | The paper mentions dataset sizes but does not provide specific training, validation, or test split percentages or counts needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, 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 | For Async-Prox SVRG, we set step size η = 0.04, the minibatch size B = 200, and the inner loop size K = 2n, where n is the data size. For Async-Prox SVRCD, we set step size η = 0.04, the number of block partitions m = d 100, the minibatch size B = 200, and a larger inner loop size K = 2nm. |