by Joerg Simon Wicker | Jun 25, 2021 | Adversarial Learning, Bias, Join us
Summary We aim to design and develop new methods to attack machine learning models and identify the lack of reliability, for example related to bias in the data and model. These issues can cause problems in various applications, caused by weak performances of models...
by Katharina Dost | May 28, 2021 | Adversarial Learning, AI Reading Group, News
Where and when: Thursday, June 10 at 2-3pm in 303S-561 Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transferability has been shown in previous...
by jkim072 | Apr 12, 2021 | Adversarial Learning, AI Reading Group, Computational Sustainability, News
For many real-world tasks obtaining a complete feature set is prohibitively expensive, especially in healthcare. Specifically, physicians must constantly balance the trade-off between predictive performance and cost for which features to observe. In this paper we...
by Joerg Simon Wicker | Feb 25, 2021 | Adversarial Learning, News, Research
Kia Ora! I am Luke Chang and I am passionate about building more reliable machine learning models, an artificial intelligence people can trust. I started my machine learning journey by designing a transmission controller for an engine in my third-year undergraduate...
by Joerg Simon Wicker | Sep 22, 2020 | Adversarial Learning, News, PYR, Seminar
Provisional Year Seminar – Xinglong (Luke) Chang – Robust Automated Adversary-Aware Machine Learning Models October 12, 2020 10:00 am - 12:00 pm Provisional Year Seminar Download iCal file for this...
by Katerina Taskova | Aug 26, 2020 | Adversarial Learning, UoA Machine Learning Seminars
Machine Learning Seminar of Dr. Ambra Demontis – “Adversarial Machine Learning: Attacking and Defending Machine Learning Systems” October 19, 2020 7:00 pm - 8:00 pm UoA Machine Learning Seminars Download iCal file for this...
