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Simone Stumpf

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Simone Stumpf

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Publications by Simone Stumpf (bibliography)

» 2009 «

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Shen, Jianqiang, Irvine, Jed, Bao, Xinlong, Goodman, Michael, Kolibaba, Stephen, Tran, Anh, Carl, Fredric, Kirschner, Brenton, Stumpf, Simone and Dietterich, Thomas G. (2009): Detecting and correcting user activity switches: algorithms and interfaces. In: Proceedings of the 2009 International Conference on Intelligent User Interfaces 2009. pp. 117-126. Available online

“The TaskTracer system allows knowledge workers to define a set of activities that characterize their desktop work. It then associates with each user-defined activity the set of resources that the user accesses when performing that activity. In order to correctly associate resources with activities and provide useful activity-related services to the user, the system needs to know the current activity of the user at all times. It is often convenient for the user to explicitly declare which activity he/she is working on. But frequently the user forgets to do this. TaskTracer applies machine learning methods to detect undeclared activity switches and predict the correct activity of the user. This paper presents TaskPredictor2, a complete redesign of the activity predictor in TaskTracer and its notification user interface. TaskPredictor2 applies a novel online learning algorithm that is able to incorporate a richer set of features than our previous predictors. We prove an error bound for the algorithm and present experimental results that show improved accuracy and a 180-fold speedup on real user data. The user interface supports negotiated interruption and makes it easy for the user to correct both the predicted time of the task switch and the predicted activity.”

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Kulesza, Todd, Wong, Weng-Keen, Stumpf, Simone, Perona, Stephen, White, Rachel, Burnett, Margaret M., Oberst, Ian and Ko, Andrew J. (2009): Fixing the program my computer learned: barriers for end users, challenges for the machine. In: Proceedings of the 2009 International Conference on Intelligent User Interfaces 2009. pp. 187-196. Available online

“The results of a machine learning from user behavior can be thought of as a program, and like all programs, it may need to be debugged. Providing ways for the user to debug it matters, because without the ability to fix errors users may find that the learned program's errors are too damaging for them to be able to trust such programs. We present a new approach to enable end users to debug a learned program. We then use an early prototype of our new approach to conduct a formative study to determine where and when debugging issues arise, both in general and also separately for males and females. The results suggest opportunities to make machine-learned programs more effective tools.”

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» 2008 «

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Stumpf, Simone, Sullivan, Erin, Fitzhenry, Erin, Oberst, Ian, Wong, Weng-Keen and Burnett, Margaret (2008): Integrating rich user feedback into intelligent user interfaces. In: Proceedings of the 2008 International Conference on Intelligent User Interfaces 2008. pp. 50-59. Available online

“The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types of some of this rich feedback seem promising for assimilation by machine learning algorithms. Following up on those findings, we ran an experiment to assess the viability of incorporating real-time keyword-based feedback in initial training phases when data is limited. We found that rich feedback improved accuracy but an initial unstable period often caused large fluctuations in classifier behavior. Participants were able to give feedback by relying heavily on system communication in order to respond to changes. The results show that in order to benefit from the user's knowledge, machine learning systems must be able to absorb keyword-based rich feedback in a graceful manner and provide clear explanations of their predictions.”

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» 2007 «

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Stumpf, Simone, Rajaram, Vidya, Li, Lida, Burnett, Margaret, Dietterich, Thomas G., Sullivan, Erin, Drummond, Russell and Herlocker, Jonathan (2007): Toward harnessing user feedback for machine learning. In: Proceedings of the 2007 International Conference on Intelligent User Interfaces 2007. pp. 82-91. Available online

“There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource -- the users themselves -- could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users had no difficulty providing generous amounts of feedback. The kinds of feedback ranged from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. The results show that user feedback has the potential to significantly improve machine learning systems, but that learning algorithms need to be extended in several ways to be able to assimilate this feedback.”

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