A Stochastic Optimization Framework for Fair Risk Minimization
Authors: Andrew Lowy, Sina Baharlouei, Rakesh Pavan, Meisam Razaviyayn, Ahmad Beirami
TMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that FERMI achieves the most favorable tradeoffs between fairness violation and test accuracy across all tested setups compared with state-of-the-art baselines for demographic parity, equalized odds, equal opportunity. These benefits are especially significant with small batch sizes and for non-binary classification with large number of sensitive attributes, making FERMI a practical, scalable fairness algorithm. The code for all of the experiments in this paper is available at: https://github.com/optimization-for-data-driven-science/FERMI. |
| Researcher Affiliation | Collaboration | Andrew Lowy EMAIL Department of Mathematics University of Southern California Sina Baharlouei EMAIL Department of Industrial & Systems Engineering University of Southern California Rakesh Pavan EMAIL Department of Computer Science University of Washington Meisam Razaviyayn EMAIL Department of Industrial & Systems Engineering, Computer Science, and Electrical Engineering University of Southern California Ahmad Beirami EMAIL Google Research |
| Pseudocode | Yes | Algorithm 1 FERMI Algorithm 1: Input: θ0 Rdθ, W 0 = 0 Rk m, step-sizes (ηθ, ηw), fairness parameter λ 0, iteration number T, minibatch sizes |Bt|, t {0, 1, , T}, W := Frobenius norm ball of radius D around 0 Rk m for D given in Appendix D. 2: Compute b P 1/2 s = diag(ˆp S(1) 1/2, . . . , ˆp S(k) 1/2). 3: for t = 0, 1, . . . , T do 4: Draw a mini-batch Bt of data points {(xi, si, yi)}i Bt 5: Set θt+1 θt ηθ |Bt| P i Bt[ θℓ(xi, yi; θt) + λ θ bψi(θt, W t)]. 6: Set W t+1 ΠW W t + 2ληw i Bt h W t E[by(xi, θ)by(xi, θ)T |xi] + b P 1/2 s E[siby T (xi; θt)|xi, si] i 7: end for 8: Pick ˆt uniformly at random from {1, . . . , T}. 9: Return: θˆt. |
| Open Source Code | Yes | The code for all of the experiments in this paper is available at: https://github.com/optimization-for-data-driven-science/FERMI. |
| Open Datasets | Yes | In Section 3.1, we showcase the performance of FERMI applied to a logistic regression model on binary classification tasks with binary sensitive attributes on Adult, German Credit, and COMPAS datasets. All of the following datasets are publicly available at UCI repository. |
| Dataset Splits | Yes | We generate ten distinct train/test sets for each one of the German and COMPAS datasets by randomly sampling 80% of data points as the training data and the rest 20% as the test data. Adult dataset contains the census information of individuals including education, gender, and capital gain. The assigned classification task is to predict whether a person earns over 50k annually. The train and test sets are two separated files consisting of 32, 000 and 16, 000 samples respectively. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, processor types, or memory amounts) are mentioned in the paper for running experiments. It mentions using 'neural network function approximation' and 'large-scale problems' which implies powerful hardware, but no specifics. |
| Software Dependencies | No | The paper mentions implementing models like logistic regression and convolutional neural networks, which implies the use of programming languages and libraries (e.g., Python, PyTorch/TensorFlow), but specific versions for these are not provided. |
| Experiment Setup | Yes | The trade-offcurves for FERMI are generated by sweeping across different values for λ [0, 10000]. The learning rates ηθ, ηw is constant during the optimization process and is chosen from the interval [0.0005, 0.01] for all datasets. Moreover, the number of iterations T for experiments in Fig. 1 is fixed to 2000. ... We use a mini-batch of size 512 when using the stochastic solver. |