Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Authors: Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Bo Li, Peng Cui
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments confirm GCDRO s superiority over conventional DRO methods. In this section, we test the empirical performances of our proposed GCDRO on simulation data and real-world regression datasets with natural distribution shifts. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Technology, Tsinghua University 2Department of Industrial Engineering and Operations Research, Columbia University 3Zhongguancun Lab 4School of Economics and Management, Tsinghua University. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement or link indicating the release of its own source code for the described methodology. |
| Open Datasets | Yes | Datasets. (1) Bike-sharing dataset (Dua & Graff, 2017)... (2) House Price dataset1... 1https://www.kaggle.com/c/house-prices-advancedregressiontechniques/data (3) Temperature dataset (Dua & Graff, 2017)... URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | Yes | In training, we generate 9,500 points with r = 1.9 (majority, strong positive spurious correlation V -Y ) and 500 points with r = 1.3 (minority, weak negative spurious correlation V -Y ). In practice, we do a grid search over α [0.1, 10] on an independent held-out validation dataset to select the best α. |
| Hardware Specification | No | The paper mentions 'GPU' generally for parallelization but does not specify any particular GPU model, CPU, or other hardware components used for experiments. |
| Software Dependencies | No | The paper mentions software like 'Py Torch' and 'DGL package (Wang et al., 2019)' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | For all these experiments, we use a two-layer MLP model with mean square error (MSE). We use the Adam optimizer (Kingma & Ba, 2015) with the default learning rate 1e 3. And all methods are trained for 5e3 epochs. The hyper-parameter search space is specified in Appendix. |