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...
The widespread implementation of machine learning in safety-critical domains has raised ethical concerns regarding algorithmic discrimination. In such sett
New 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
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. ...
The company offers a software framework that enables businesses to deploy, monitor, and manage machine learning models. Seldon's products cater to a variety of industries that require robust machine learning operations, including financial services, automotive, and insurance sectors. It was founded in...
Machine learning has the opportunity to fundamentally change the face of clinical healthcare. However, the challenges of delivering fair and equitable treatment using algorithms have been under-explored. The goal of this workshop is to investigate issues around fairness and other ethical issues that ...
《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...