Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Authors: Shuai Zheng, James Tin-Yau Kwok
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform experiments on logistic regression (Section 4.1) and AUC maximization (Section 4.2). |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. |
| Pseudocode | Yes | Algorithm 1 Stochastic sample-average gradient (SSAG). Algorithm 2 Stochastic SAGA (S-SAGA). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is available. |
| Open Datasets | Yes | Experiments are performed on two highdimensional data sets from the LIBSVM archive (Table 2). |
| Dataset Splits | No | Table 2 provides '#training' and '#testing' sample counts for the datasets used, but the paper does not specify the splitting methodology (e.g., exact percentages, random seed, or specific predefined splits) for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | The dropout probability p = 0.3... We vary λ {10 6, 10 7, 10 8}... We use a slightly larger βt = t 0.75... The stepsize schedule is ηt = c/(γ + t). We fix c = 2/λ for SGD, SSAG, S-SAGA, and c = 2n for S-MISO... |