no code implementations • 13 May 2024 • Ebuka Okpala, Nishant Vishwamitra, Keyan Guo, Song Liao, Long Cheng, Hongxin Hu, Yongkai Wu, Xiaohong Yuan, Jeannette Wade, Sajad Khorsandroo
While capstone projects are an excellent example of experiential learning, given the interdisciplinary nature of this emerging social cybersecurity problem, it can be challenging to use them to engage non-computing students without prior knowledge of AI.
1 code implementation • 20 Jan 2024 • Yaowei Hu, Yongkai Wu, Lu Zhang
This paper studies long-term fair machine learning which aims to mitigate group disparity over the long term in sequential decision-making systems.
no code implementations • 15 Dec 2023 • Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma, Yongkai Wu
To address this, we define a fair pruning task where a sparse model is derived subject to fairness requirements.
1 code implementation • 29 Oct 2023 • Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang, Jingwei Sun, Qilin Zheng, Yongkai Wu, Ang Li, Hai "Helen" Li, Yiran Chen
Specifically, SiDA-MoE attains a remarkable speedup in MoE inference with up to $3. 93\times$ throughput increasing, up to $72\%$ latency reduction, and up to $80\%$ GPU memory saving with down to $1\%$ performance drop.
no code implementations • 17 Oct 2023 • Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen
Deep generative models have shown tremendous capability in data density estimation and data generation from finite samples.
no code implementations • 28 Sep 2023 • Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
Anomaly detection in multivariate time series has received extensive study due to the wide spectrum of applications.
1 code implementation • 15 Sep 2023 • Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu
We focus on a decentralized notion of Differential Privacy, namely Local Differential Privacy, and apply randomization mechanisms to perturb both feature and label data at the node level before the data is collected by a central server for model training.
1 code implementation • 2 Jun 2023 • Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu
Further, to promote the disentanglement of causal factors, we propose a causal disentanglement prior learned from auxiliary labels and the latent causal structure.
1 code implementation • 4 Mar 2023 • Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings.
1 code implementation • 8 Dec 2022 • Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal.
1 code implementation • NeurIPS 2020 • Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
Previous research in fair classification mostly focuses on a single decision model.
no code implementations • 11 Nov 2019 • Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu
We develop algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort.
no code implementations • NeurIPS 2019 • Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong
A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions.
no code implementations • 13 Sep 2018 • Yongkai Wu, Lu Zhang, Xintao Wu
In this paper, we propose a general framework for learning fair classifiers which addresses previous limitations.
no code implementations • 5 Mar 2018 • Yongkai Wu, Lu Zhang, Xintao Wu
Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal.
no code implementations • 28 Feb 2017 • Lu Zhang, Yongkai Wu, Xintao Wu
Based on the results, we develop a two-phase framework for constructing a discrimination-free classifier with a theoretical guarantee.
no code implementations • 22 Nov 2016 • Lu Zhang, Yongkai Wu, Xintao Wu
In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data is used for predictive analysis (e. g., building classifiers).
no code implementations • 22 Nov 2016 • Lu Zhang, Yongkai Wu, Xintao Wu
Discrimination discovery and prevention/removal are increasingly important tasks in data mining.