Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |