We propose online decision strategies for time-dependent sequences of linear programs which use no distributional and minimal geometric assumptions about the data. These strategies...
Tatsiana Levina, Yuri Levin, Jeff McGill, Mikhail ...
We apply robust Bayesian decision theory to improve both generative and discriminative learners under bias in class proportions in labeled training data, when the true class propo...
In this paper, we introduce a system named Argo which provides intelligent advertising made possible from users’ photo collections. Based on the intuition that user-generated ph...
Xin-Jing Wang, Mo Yu, Lei Zhang, Rui Cai, Wei-Ying...
Abstract. With widespread use of microarray technology as a potential diagnostics tool, the comparison of results obtained from the use of different platforms is of interest. When...
This paper addresses the problem of learning archetypal structural models from examples. To this end we define a generative model for graphs where the distribution of observed nod...