Fairness without Harm: An Influence-Guided Active Sampling Approach
Authors: Jinlong Pang, Jialu Wang, Zhaowei Zhu, Yuanshun Yao, Chen Qian, Yang Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world data demonstrate the effectiveness of our proposed algorithm. |
| Researcher Affiliation | Collaboration | Jinlong Pang UC Santa Cruz jpang14@ucsc.edu Jialu Wang UC Santa Cruz faldict@ucsc.edu Zhaowei Zhu Docta.ai zzw@docta.ai Yuanshun Yao Meta Gen AI kevinyao@meta.com Chen Qian UC Santa Cruz cqian12@ucsc.edu Yang Liu UC Santa Cruz yangliu@ucsc.edu |
| Pseudocode | Yes | Algorithm 1 Fair influential sampling (FIS) |
| Open Source Code | Yes | Our code is available at github.com/UCSC-REAL/Fairness Without Harm. |
| Open Datasets | Yes | We evaluate the performance of our algorithm on three real-world datasets across three different modalities: Celeb A [49], UCI Adult [8] and Compas [6]. |
| Dataset Splits | Yes | Then, the test dataset is split into two independent portions: a new test set and a validation set, with 10% of the test data randomly designated as the hold-out validation set. |
| Hardware Specification | No | The paper mentions 'running one experiment for the Celeb A dataset on a single GPU roughly requires about 4 hours' but does not specify the model or type of GPU. |
| Software Dependencies | No | The paper mentions using 'SGD optimizer' and 'Re LU network' but does not list specific software libraries or their version numbers (e.g., PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | The epochs are split into two phases: warm-up epochs (5 epochs) and training epochs (10 epochs). The default label budget per round, which represents the number of solicited data samples, is set to 256. Additionally, the default values for learning rate, momentum, and weight decay are 0.01, 0.9, and 0.0005, respectively. |