How does overparametrization affect performance on minority groups?
Authors: Saptarshi Roy, Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we complement these empirical studies with a theoretical investigation of the risk of overparameterized random feature regression models on minority groups... In an experiment with California housing prices dataset 2 (see Appendix D)... we also provide a simulation study for the effect of overparameterization on for classifications with random features. |
| Researcher Affiliation | Collaboration | Saptarshi Roy EMAIL Department of Computer Science University of Texas at Austin; Subha Maity EMAIL Department of Statistics & Actuarial Science University of Waterloo; Songkai Xue EMAIL Department of Statistics University of Michigan, Ann Arbor; Mikhail Yurochkin EMAIL MIT-IBM Watson AI Lab; Yuekai Sun EMAIL Department of Statistics University of Michigan, Ann Arbor |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. It primarily presents mathematical derivations and theoretical analyses. |
| Open Source Code | Yes | 1Codes are available at https://github.com/smaityumich/overparameterized-group-fairness. |
| Open Datasets | Yes | In an experiment with California housing prices dataset 2 (see Appendix D)... 2https://www.kaggle.com/datasets/camnugent/california-housing-prices |
| Dataset Splits | Yes | Furthermore, we split the data into training (80%) and test (20%) datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In the simulation for Figure 8 we let σ( ) be the Re LU activation function and θi,j be IID standard normal distributed. Moreover, we let π = 0.95, β0 = 10e1, β1 = 10 cos(θ)e1 + 10 sin(θ)e2, n = 400, d = 200, N = γn where e1 and e2 are the first two standard basis of Rd. We tune hyperparameters θ {0 , 45 , 90 , 135 , 180 } and γ {0.5, 1, . . . , 3}, then report test errors averaged over 20 replicates. |