Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems
Authors: Yi Xu, Rong Jin, Tianbao Yang
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments Regularized loss minimization. First, we compare MB-SPG, SPGR with MBSGA, VRSGA, SSDC-SPG and SSDC-SVRG for solving the regularized non-linear least square (NLLS) classification problems [...] Two data sets (covtype and a9a) are used for classification, and two data sets E2006 and triazines are used for regression. |
| Researcher Affiliation | Collaboration | Yi Xu1, Rong Jin2, Tianbao Yang1 1. Department of Computer Science, The University of Iowa, Iowa City, IA 52246, USA 2. Machine Intelligence Technology, Alibaba Group, Bellevue, WA 98004, USA |
| Pseudocode | Yes | Algorithm 1 Mini-Batch Stochastic Proximal Gradient: MB-SPG |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | Two data sets (covtype and a9a) are used for classification, and two data sets E2006 and triazines are used for regression. These data sets are downloaded from the libsvm website. |
| Dataset Splits | No | The paper mentions using well-known datasets but does not explicitly specify the exact train/validation/test splits (e.g., percentages, absolute counts, or cross-validation scheme) used for experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) 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 like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | The truncation value α is set to 10n following [44]. The value of regularization parameter λ is fixed as 10 4 and the value of κ is fixed as 0.2d where d is the dimension of data. For all algorithms, we use the theoretical values of the parameters for the sake of fairness in comparison. All algorithms start with the same initial solution with all zero entries. We implement the increasing mini-batch versions of MB-SPG and SPGR (online setting) with b = 1. |