Efficient Stochastic Approximation of Minimax Excess Risk Optimization
Authors: Lijun Zhang, Haomin Bai, Wei-Wei Tu, Ping Yang, Yao Hu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments to validate the efficiency and effectiveness of our algorithms. |
| Researcher Affiliation | Collaboration | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2Pazhou Laboratory (Huangpu), Guangzhou, China 3Artificial Productivity Inc., Beijing, China 4Xiaohongshu Inc., Beijing, China. |
| Pseudocode | Yes | Algorithm 1 An Anytime Stochastic Approximation Approach for MERO |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We additionally utilize the Adult dataset (Becker & Kohavi, 1996) |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, required to replicate the experiment. |
| Experiment Setup | No | The paper describes the general approach (e.g., logistic loss, linear model, fixed step size) but does not provide concrete hyperparameter values or detailed system-level training settings for the experiments. |