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 [1].
Multi-group Learning for Hierarchical Groups
Authors: Samuel Deng, Daniel Hsu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then conduct an empirical evaluation of our algorithm and find that it achieves attractive generalization properties on real datasets with hierarchical group structure. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Columbia University. Correspondence to: Samuel Deng <EMAIL>, Daniel Hsu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 MGL-Tree |
| Open Source Code | No | The paper refers to a third-party 'open-source xgboost implementation' but does not state that the code for their own methodology is open-source or provide a link to it. |
| Open Datasets | Yes | We conduct our experiments on twelve U.S. Census datasets from the Folktables package of Ding et al. (2021). |
| Dataset Splits | No | The paper mentions using 'a held-out test set of 20% of the data' but does not specify a train/validation/test split or cross-validation details for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper mentions using 'scikit-learn' and 'xgboost' implementations but does not specify their version numbers. |
| Experiment Setup | Yes | Model Hyperparameters Logistic Regression loss = log loss, dual=False, solver=lbfgs Decision Tree criterion = log loss, max depth = {2, 4, 8} Random Forest criterion = log loss XGBoost objective = binary:logistic |