This paper is concerned with a new task of ranking, referred to as "supplementary data assisted ranking", or "supplementary ranking" for short. Different from c...
This paper studies the problem of learning from ambiguous supervision, focusing on the task of learning semantic correspondences. A learning problem is said to be ambiguously supe...
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing...
Olivier Chapelle, Pannagadatta K. Shivaswamy, Srin...
We propose a general and efficient algorithm for learning low-rank matrices. The proposed algorithm converges super-linearly and can keep the matrix to be learned in a compact fac...
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...