Towards Reducing Biases in Combining Multiple Experts Online
Authors: Yi Sun, Iván Ramírez Díaz, Alfredo Cuesta Infante, Kalyan Veeramachaneni
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also demonstrate the performance of the algorithm on real data sets commonly used by the fairness community. |
| Researcher Affiliation | Academia | Yi Sun1 , Iv an Ram ırez D ıaz1 , Alfredo Cuesta-Infante2 and Kalyan Veeramachaneni1 1 MIT 2University Rey Juan Carlos {yis, iramdia}@mit.edu, alfredo.cuesta@urjc.es, kalyan@csail.mit.edu |
| Pseudocode | No | The paper includes 'Figure 1: This figure shows how G-FORCE process an input pair (x, z)', which illustrates the algorithm's mechanism through a diagram and descriptive steps, but it is not presented as formal pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We consider the Adult, German Credit and COMPAS datasets, all of which are commonly used by the fairness community. |
| Dataset Splits | No | The paper states, 'These classifiers are trained using 70% of the data set. The remaining 30% of the dataset is set aside to simulate the online arrival of individuals,' but does not explicitly describe a separate validation split or cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, or memory) used to conduct the experiments. |
| Software Dependencies | No | The paper lists the types of classifiers used (Logistic Regression, SVM, Decision Tree, MLP), but does not specify the software libraries or their version numbers used for implementation or experimentation. |
| Experiment Setup | Yes | We use η = 0.35 in experiments. For each dataset, we repeat the experiments 100 times, each with 10000 samples from a specific distribution setting. We repeated the experiments 1000 times for German and COMPAS, as well as 10 times for Adult, by randomizing the arrival sequence of individuals. |