Fast and Efficient MMD-Based Fair PCA via Optimization over Stiefel Manifold
Authors: Junghyun Lee, Gwangsu Kim, Mahbod Olfat, Mark Hasegawa-Johnson, Chang D. Yoo7363-7371
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental comparisons based on synthetic and UCI datasets show that our approach outperforms prior work in explained variance, fairness, and runtime. |
| Researcher Affiliation | Collaboration | Junghyun Lee1, Gwangsu Kim*2, Mahbod Olfat3,4, Mark Hasegawa-Johnson5, Chang D. Yoo*2 1 Kim Jaechul Graduate School of AI, KAIST, Seoul, Republic of Korea 2 School of Electrical Engineering, KAIST, Daejeon, Republic of Korea 3 UC Berkeley IEOR, Berkeley, CA, USA 4 Citadel, Chicago, IL, USA 5 Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, IL, USA |
| Pseudocode | Yes | Algorithm 1: REPMS for Mb F-PCA |
| Open Source Code | Yes | Codes are available in our Github repository6. https://github.com/nick-jhlee/fair-manifold-pca |
| Open Datasets | Yes | COMPAS dataset (Kirchner et al. 2016), Adult income dataset, and German credit dataset. See Section I of the SP for complete description of the pre-processing steps. For both algorithms, we consider two different hyperparameter settings |
| Dataset Splits | Yes | For all experiments, we considered 10 different 70/30 train-test splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for its experiments. |
| Software Dependencies | Yes | For FPCA, we use the same Python MOSEK(Ap S 2021) implementation as provided by (Olfat and Aswani 2019). (Reference [Ap S 2021] is 'MOSEK Optimizer API for Python. Version 9.2.36. MOSEK.') |
| Experiment Setup | Yes | We’ve set K = 100, ϵmin = 10-6, ϵ0 = 10-1, θϵ = (ϵmin/ϵ0)1/5, ρmax = 1010, θρ = 2, dmin = 10-6. |