The rise of machine-learning applications in domains with critical end-user impact has led to a growing concern about the fairness of learned models, with the goal of avoiding biases that negatively impact specific demographic groups. Most existing bias-mitigation strategies adapt the im...
Many CDSSs utilising machine learning have been developed to assist with other aspects of antimicrobial use17,18,19; however, limited clinical utilisation and adoption has been seen20. As such, when tackling this problem we wanted to ensure our CDSS solution was simple, fair, interpretable, and...
Statistics - Machine LearningNew social and economic activities massively exploit big data and machine learning algorithms to do inference on people's lives. Applications include automatic curricula evaluation, wage determination, and risk assessment for credits and loans. Recently, many governments and ...
The widespread implementation of machine learning in safety-critical domains has raised ethical concerns regarding algorithmic discrimination. In such sett
In the recent years, I developed new tools for applications of optimal transport in machine learning and statistics, including new tests for classification using Wasserstein distance and statistical properties of Fréchet means of distributions seen as Wasserstein Barycenters. This work has important develo...
[20] Z. Chai, et al., “CircuitNet: An open-source dataset for machine learning applications in electronic design automation,” SCIS, 2022. [21] J. Pan, et al., “Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data,” DAC, 2022. ...
Optimize your environments with real-time data for machine learning and analytics. Avoid data bottlenecks with parallel processing between applications. Simple JSON Configurations MQ offers both plug-and-play and JSON configurations, making it easy to set up and operate. Empower your whole factory to...
As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining what "unfairness" should mean in a given context is non-trivial: there are many competing definitions, and choosing...
《Federated Machine Learning: Concept and Applications》 《 Federated Learning: Challenges, Methods, and Future Directions》 《联邦学习概述:技术、应用及未来_李少波》 《Survey of Personalization Techniques for Federated Learning》 自己用的10个论文下载网站,这10个绝对够! FL相关的论文、网页、博客、视频等Gi...
As the data-driven decision process becomes dominating for industrial applications, fairness-aware machine learning arouses great attention in various areas. This work proposes fairness penalties learned by neural networks with a simple random sampler of sensitive attributes for non-discriminatory supervised...