Impactx2 Logo and Photo

Impact x2 Qais

Impactx2 Content

How can we work together to promote better cultural understanding worldwide?

Qais Akbar Omar (GRS’16), a graduate student in the Creative Writing Program, has published a much-praised memoir, A Fort of Nine Towers: An Afghan Family Story. He recalls how the violence and tumult of civil war jolted his family, who, despite losing relatives, their home, and possessions, continued to nurture his wish to attend a university.

Impactx2 Call to Action

With your help, students like Qais gain the skills they need to tell their story and give us a broader understanding of the world.

Will you support CAS?

Clayton Scott - University of Michigan

Starts:
4:00 pm on Thursday, November 29, 2012
Ends:
5:00 pm on Thursday, November 29, 2012
Location:
MCS 148
Title: Classification with Asymmetric Label Noise. Abstract: In many real-world classification problems, the labels of training examples are randomly corrupted. That is, the set of training examples for each class is contaminated by examples of the other class. Existing approaches to this problem assume that the two classes are separable, that the label noise is independent of the true class label, or that the noise proportions for each class are known. We introduce a general framework for classification with label noise that eliminates these assumptions. In particular, we identify necessary and sufficient distributional assumptions for the existence of a consistent estimator of the optimal risk, with associated estimation strategies. We find that learning in the presence of label noise is possible even when the class-conditional distributions overlap and the label noise is not symmetric. A key to our approach is a universally consistent estimator of the maximal proportion of one distribution that is present in another, or equivalently, of the so-called "separation distance" between two distributions. The methodology is motivated by a problem in nuclear particle classification.