This paper is concerned with the generalization ability of learning to rank algorithms for information retrieval (IR). We point out that the key for addressing the learning proble...
Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, Hang...
One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures used in i...
In a seminal paper, Amari (1998) proved that learning can be made more efficient when one uses the intrinsic Riemannian structure of the algorithms' spaces of parameters to po...
An algorithm based on the Generalized Hebbian Algorithm is described that allows the singular value decomposition of a dataset to be learned based on single observation pairs pres...
In many image and video collections, we have access
only to partially labeled data. For example, personal photo
collections often contain several faces per image and a caption
t...
Benjamin Sapp, Benjamin Taskar, Chris Jordan, Timo...