Stochastic Gradient Boosted Decision Trees (GBDT) is one of the most widely used learning algorithms in machine learning today. It is adaptable, easy to interpret, and produces highly accurate models. However, most implementations today are computationally expensive and require all training data to be in main memory. As training data becomes ever larger, there is motivation for us to parallelize the GBDT algorithm. Parallelizing decision tree training is intuitive and various approaches have been explored in existing literature. Stochastic boosting on the other hand is inherently a sequential process and have not been applied to distributed decision trees. In this work, we present two different distributed methods that generates exact stochastic GBDT models, the first is a MapReduce implementation and the second utilizes MPI on the Hadoop grid environment.

http://biosearch.berkeley.edu.

This paper presents the results of a pilot usability study of a novel approach to search user interfaces for bioscience journal articles. The main idea is to support search over figure captions explicitly, and show the corresponding figures directly within the search results. Participants in a pilot study expressed surprise at the idea, noting that they had never thought of search in this way. They also reported strong positive reactions to the idea: 7 out of 8 said they would use a search system with this kind of feature, suggesting that this is a promising idea for journal article search.