4 papers · 1 filter
FairSAM: Fair Classification on Corrupted Image Data Through Sharpness-Aware Minimization
Yucong Dai, Jie Ji, Xiaolong Ma +1
Image classification models trained on clean data often degrade sharply when exposed to corrupted test or deployment data, such as images with impulse noise, Gaussian noise, or env…
Towards counterfactual fairness through auxiliary variables
Bowei Tian, Ziyao Wang, Shwai He +5
The challenge of balancing fairness and predictive accuracy in machine learning models, especially when sensitive attributes such as race, gender, or age are considered, has motiva…
Integrating Fairness and Model Pruning Through Bi-level Optimization
Yucong Dai, Gen Li, Feng Luo +2
Deep neural networks have achieved exceptional results across a range of applications. As the demand for efficient and sparse deep learning models escalates, the significance of mo…
SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
Zhixu Du, Shiyu Li, Yuhao Wu +7
Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable…