Fairness-Aware Meta-Learning via Nash Bargaining
Authors: Yi Zeng, Xuelin Yang, Li Chen, Cristian Ferrer, Ming Jin, Michael Jordan, Ruoxi Jia
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is supported by theoretical results, notably a proof of the NBS for gradient aggregation free from linear independence assumptions, a proof of Pareto improvement, and a proof of monotonic improvement in validation loss. We also show empirical effects across various fairness objectives in six key fairness datasets and two image classification tasks. |
| Researcher Affiliation | Collaboration | Yi Zeng 1, Xuelin Yang 2, Li Chen3, Cristian Canton Ferrer3, Ming Jin1, Michael I. Jordan2, Ruoxi Jia1 1Virginia Tech, Blacksburg, VA 24061, USA 2University of California, Berkeley, CA 94720, USA 3Meta AI, Menlo Park, CA 94025, USA |
| Pseudocode | Yes | Algorithm 1: Two-stage Nash-Meta-Learning Training |
| Open Source Code | Yes | Code: Nash-Meta-Learning. ... We provide the code at https://github.com/reds-lab/ Nash-Meta-Learning with detailed instruction included. |
| Open Datasets | Yes | We test our method on six standard fairness datasets across various sectors of fairness tasks: financial services (Adult Income [3], Credit Default [54]), marketing (Bank Telemarketing [33]), criminal justice (Communities and Crime [42]), education (Student Performance [11]), and disaster response (Titanic Survival [12]). |
| Dataset Splits | Yes | Test sets comprise 3% of each dataset (10% for the student performance dataset with 649 samples) by randomly selecting a demographically and label-balanced subset. See Table 2 in Appendix A.6 for data distribution specifics. |
| Hardware Specification | Yes | All experiments were conducted on an internal cluster using one chip of H-100. |
| Software Dependencies | No | The paper mentions software like ResNet-18 and uses Python-based tools implied by the GitHub link, but it does not specify exact version numbers for any software libraries, frameworks, or dependencies used in the experiments. |
| Experiment Setup | Yes | Common hyperparameters across all algorithms include a total of 50 training epochs, an SGD optimizer momentum of 0.9, and a weight decay of 5e-4, with the bargaining phase limited to 15 epochs for the three settings incorporating proposed Nash-Meta-Learning. Hyperparameters that varied are detailed in Table 3. |