Towards Interpretability and Robustness of Machine Learning
Jianbo Chen, University of California, Berkeley
2019-07-26 14:30:00 ~ 2019-07-26 16:00:00
Room 1319, Software Expert Building
Quanshi Zhang, Associate Professor, John Hopcroft Center for Computer Science
Interpretability and robustness become important criteria when a machine learning model is applied in critical areas such as medicine, financial markets, and criminal justice. Many complex models, such as random forests and deep neural networks, have been developed and employed to optimize prediction accuracy. However, their complex and black-box nature leads to difficulty in the interpretation of the decision making process, and vulnerability under minimal adversarial perturbation.