FEAMOE: Fair, Explainable and Adaptive Mixture of Experts
Authors: Shubham Sharma, Jette Henderson, Joydeep Ghosh
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple datasets show that our framework as applied to a mixture of linear experts is able to perform comparably to neural networks in terms of accuracy while producing fairer models. |
| Researcher Affiliation | Collaboration | Shubham Sharma1 , Jette Henderson2 and Joydeep Ghosh1 1The University of Texas at Austin 2Tecno Tree {shubham_sharma, jghosh}@utexas.edu, jette.henderson@gmail.com |
| Pseudocode | Yes | Algorithm 1 Learning FEAMOE |
| Open Source Code | Yes | We show experimentally (in the supplementary material 1) that FEAMOE can work comparably or better to adapt for drift. 1https://drive.google.com/file/d/1l2qz50Flvj4VAEvr Rr H4Gdy3QAm CRn Y/view?usp=sharing |
| Open Datasets | Yes | UCI Adult [Kohavi, 1996] and COMPAS [Pro Publica, 2016]. The large HMDA (Home Mortgage Disclosure Act) dataset [Bureau, 2020] |
| Dataset Splits | No | No specific dataset splits (e.g., percentages for training, validation, and test sets) are provided in the main text. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud computing instance types) are provided for the experimental setup. |
| Software Dependencies | No | The paper mentions "scikit learn" but does not provide a specific version number. No other software dependencies with version numbers are listed. |
| Experiment Setup | Yes | A two layer multilayer perceptron with 30 hidden units in each layer was trained for the UCI Adult dataset. A five layer multilayer perceptron with 50 hidden units in each layer is trained for the HMDA dataset as the baseline neural network. Experts are added every 4000 data points for the UCI Adult dataset. Hyperparameters associated with the fairness constraints are incremented in levels of 0.02 per expert for the UCI Adult dataset. |