Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond
Authors: Dingzhi Yu, Yunuo Cai, Wei Jiang, Lijun Zhang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct numerical experiments on empirical GDRO and empirical MERO to evaluate the performance of our algorithms. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2School of Data Science, Fudan University, Shanghai, China 3Pazhou Laboratory (Huangpu), Guangzhou, China. |
| Pseudocode | Yes | Algorithm 1 Variance-Reduced Stochastic Mirror Prox Algorithm for Empirical GDRO (ALEG) ... Algorithm 2 Two-Stage Algorithm for Empirical MERO (ALEM) |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | For the real-world dataset, we use CIFAR-100 (Krizhevsky et al., 2009) |
| Dataset Splits | No | The paper mentions '500 training images and 100 testing images for each class' for CIFAR-100 but does not specify a separate validation split or dataset. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or cloud computing resources used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Under conditions in Theorem 4.4, by setting K = Θ( n), the computation complexity for Algorithm 1 to reach ε-accuracy of (3) is O m ε . |