We experimentally study on-line investment algorithms first proposed by Agarwal and Hazan and extended by Hazan et al. which achieve almost the same wealth as the best constant-re...
Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. S...
Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datas...
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PC...
Chris H. Q. Ding, Ding Zhou, Xiaofeng He, Hongyuan...
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a BN, however, is typically of high computational complexity. In this paper, we e...
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...