3 accepted papers
Fairness and robustness are critical elements of trustworthy machine learning systems that need to be addressed. Using a minimax framework, in this paper, we aim to design an adversarially robust fair regression model that achieves optimal performance in the presence of an attacker who is able to pe…
In this paper, we develop a framework to achieve a desirable trade-off between fairness, inference accuracy and privacy protection in the inference as service scenario. Instead of sending raw data to the cloud, we conduct a random mapping of the data, which will increase privacy protection and mitig…
In this paper, we propose a general framework to provide a desirable trade-off between inference accuracy and privacy protection in the inference as service scenario. Instead of sending data directly to the server, the user will preprocess the data through a privacy-preserving mapping, which will in…