We propose a fast batch learning method for linearchain Conditional Random Fields (CRFs) based on Newton-CG methods. Newton-CG methods are a variant of Newton method for high-dime...
Yuta Tsuboi, Yuya Unno, Hisashi Kashima, Naoaki Ok...
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...
Action modeling is an important skill for agents that must perform tasks in novel domains. Previous work on action modeling has focused on learning STRIPS operators in discrete, r...
Utility elicitation is an important component of many applications, such as decision support systems and recommender systems. Such systems query users about their preferences and ...
Previous research in Artificial Intelligence has identified the possibility of simplifying planning problems via the identification and exploitation of symmetries. We advance t...
Behind every search query is a high-level mission that the user wants to accomplish. While current search engines can often provide relevant information in response to well-speci...
End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can cr...
Saleema Amershi, James Fogarty, Ashish Kapoor, Des...