We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classi...
We propose a multi-sensor affect recognition system and evaluate it on the challenging task of classifying interest (or disinterest) in children trying to solve an educational pu...
Introductory computer science classes are known for having a high attrition rate. Some authors believe this is due to students’ difficulties in establishing a mental model of co...
This paper introduces an innovative approach for automated negotiating using the gender of human opponents. Our approach segments the information acquired from previous opponents,...
We show that the log-likelihood of several probabilistic graphical models is Lipschitz continuous with respect to the p-norm of the parameters. We discuss several implications ...