Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives
Authors: Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive empirical evaluation on four different machine learning model tuning tasks |
| Researcher Affiliation | Collaboration | Shaokun Zhang1, Feiran Jia1, Chi Wang2, Qingyun Wu1 1 Pennsylvania State University, State College, PA, USA 2 Microsoft Research, Redmond, Washington, USA |
| Pseudocode | Yes | Algorithm 1: Lexi Flow |
| Open Source Code | Yes | The implementation of our method is available in the opensource Auto ML library FLAML1. 1Link to the documentation page of Lexi Flow in FLMAL: https://microsoft.github.io/ FLAML/docs/Use-Cases/Tune-User-Defined-Function#lexicographic-objectives. code example demonstrating the use of Lexi Flow to find accurate and fast neural networks: https: //microsoft.github.io/FLAML/docs/Examples/Tune-Lexicographic-objectives. |
| Open Datasets | Yes | All datasets used in our experiment are available in Open ML. |
| Dataset Splits | Yes | Table 6: Date statistics information # of train instance # of val instance |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments were provided. |
| Software Dependencies | No | The paper mentions software libraries like FLAML, Optuna, and Scikit-learn, but does not provide specific version numbers for these or other ancillary software components used in the experiments. |
| Experiment Setup | Yes | The detailed search space in tuning Neural Networks, Random Forest, and XGboost are shown in Table 3, Table 4 and Table 5, respectively. |