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Carlos Guestrin

 

Publications by Carlos Guestrin (bibliography)

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2012
 
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Shahaf, Dafna, Guestrin, Carlos and Horvitz, Eric (2012): Trains of thought: generating information maps. In: Proceedings of the 2012 International Conference on the World Wide Web 2012. pp. 899-908. Available online

When information is abundant, it becomes increasingly difficult to fit nuggets of knowledge into a single coherent picture. Complex stories spaghetti into branches, side stories, and intertwining narratives. In order to explore these stories, one needs a map to navigate unfamiliar territory. We propose a methodology for creating structured summaries of information, which we call metro maps. Our proposed algorithm generates a concise structured set of documents maximizing coverage of salient pieces of information. Most importantly, metro maps explicitly show the relations among retrieved pieces in a way that captures story development. We first formalize characteristics of good maps and formulate their construction as an optimization problem. Then we provide efficient methods with theoretical guarantees for generating maps. Finally, we integrate user interaction into our framework, allowing users to alter the maps to better reflect their interests. Pilot user studies with a real-world dataset demonstrate that the method is able to produce maps which help users acquire knowledge efficiently.

© All rights reserved Shahaf et al. and/or ACM Press

2007
 
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Mutlu, Bilge, Krause, Andreas, Forlizzi, Jodi, Guestrin, Carlos and Hodgins, Jessica (2007): Robust, Lowcost, Non-intrusive Sensing and Recognition of Seated Postures. In: Proceedings of 20th ACM Symposium on User Interface Software and Technology October 7-10, 2007, 2007, Newport, RI, USA. . Available online

In this paper, we present a methodology for recognizing seated postures using data from pressure sensors installed on a chair. Information about seated postures could be used to help avoid adverse effects of sitting for long periods of time or to predict seated activities for a human-computer interface. Our system design displays accurate near-real-time classification performance on data from subjects on which the posture recognition system was trained by using a set of carefully designed, subject-invariant signal features. By using a near-optimal sensor placement strategy, we keep the number of required sensors low thereby reducing cost and computational complexity. We evaluated the performance of our technology using a series of empirical methods including (1) cross-validation (classification accuracy of 87% for ten postures using data from 31 sensors), and (2) a physical deployment of our system (78% classification accuracy using data from 19 sensors).

© All rights reserved Mutlu et al. and/or ACM

 
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