69 citations · 181 across the 15 of their papers we have counts for
15 papers
IPProtect: protecting the intellectual property of visual datasets during data valuation
Gursimran Singh, Chendi Wang, Ahnaf Tazwar +2
Data trading is essential to accelerate the development of data-driven machine learning pipelines. The central problem in data trading is to estimate the utility of a seller's data…
Knowledge-Injected Federated Learning
Zhenan Fan, Zirui Zhou, Jian Pei +4
Federated learning is an emerging technique for training models from decentralized data sets. In many applications, data owners participating in the federated learning system hold…
Revealing Unfair Models by Mining Interpretable Evidence
Mohit Bajaj, Lingyang Chu, Vittorio Romaniello +5
The popularity of machine learning has increased the risk of unfair models getting deployed in high-stake applications, such as justice system, drug/vaccination design, and medical…
Cosine Model Watermarking Against Ensemble Distillation
Laurent Charette, Lingyang Chu, Yizhou Chen +3
Many model watermarking methods have been developed to prevent valuable deployed commercial models from being stealthily stolen by model distillations. However, watermarks produced…
Mining Minority-class Examples With Uncertainty Estimates
Gursimran Singh, Lingyang Chu, Lanjun Wang +3
In the real world, the frequency of occurrence of objects is naturally skewed forming long-tail class distributions, which results in poor performance on the statistically rare cla…
An Optimal Resource Allocator of Elastic Training for Deep Learning Jobs on Cloud
Liang Hu, Jiangcheng Zhu, Zirui Zhou +3
Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users with computational resources to train their deep learning jobs. Elastic training is a service e…
Improving Fairness for Data Valuation in Horizontal Federated Learning
Zhenan Fan, Huang Fang, Zirui Zhou +4
Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federat…
Achieving Model Fairness in Vertical Federated Learning
Changxin Liu, Zhenan Fan, Zirui Zhou +4
Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learn…
Training Fair Models in Federated Learning without Data Privacy Infringement
Xin Che, Jingdi Hu, Zirui Zhou +2
Training fair machine learning models becomes more and more important. As many powerful models are trained by collaboration among multiple parties, each holding some sensitive data…
Auto-Split: A General Framework of Collaborative Edge-Cloud AI
Amin Banitalebi-Dehkordi, Naveen Vedula, Jian Pei +3
In many industry scale applications, large and resource consuming machine learning models reside in powerful cloud servers. At the same time, large amounts of input data are collec…
Finding Representative Interpretations on Convolutional Neural Networks
Peter Cho-Ho Lam, Lingyang Chu, Maxim Torgonskiy +3
Interpreting the decision logic behind effective deep convolutional neural networks (CNN) on images complements the success of deep learning models. However, the existing methods c…
Data Pricing in Machine Learning Pipelines
Zicun Cong, Xuan Luo, Pei Jian +2
Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, s…
Robust Counterfactual Explanations on Graph Neural Networks
Mohit Bajaj, Lingyang Chu, Zi Yu Xue +4
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuitio…
Decentralized Composite Optimization in Stochastic Networks: A Dual Averaging Approach with Linear Convergence
Changxin Liu, Zirui Zhou, Jian Pei +2
Decentralized optimization, particularly the class of decentralized composite convex optimization (DCCO) problems, has found many applications. Due to ubiquitous communication cong…
Personalized Cross-Silo Federated Learning on Non-IID Data
Yutao Huang, Lingyang Chu, Zirui Zhou +4
Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We…