Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyond
Authors: Dingzhi Yu, Yunuo Cai, Wei Jiang, Lijun Zhang
ICML 2024 | Venue PDF | 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 . |