We consider the minimum error entropy (MEE) criterion and an empirical risk minimization learning algorithm when an approximation of Rényi's entropy (of order 2) by Parzen windowing is minimized. This learning algorithm involves a Parzen windowing scaling parameter. We present a learning theory app
Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical foundations, or do not scale. We present a lea
Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this p
Algorithms for Markov boundary discovery from data constitute an important recent development in machine learning, primarily because they offer a principled solution to the variable/feature selection problem and give insight on local causal structure. Over the last decade many sound algorithms have
https://jmlr.csail.mit.edu/papers/volume15/delgado14a/delgado14a.pdf
https://jmlr.csail.mit.edu/papers/v14/hu13a.html
https://jmlr.csail.mit.edu/papers/v14/slivkins13a.html
https://jmlr.csail.mit.edu/papers/v14/mukherjee13a.html
https://jmlr.csail.mit.edu/papers/v14/statnikov13a.html