Thomas G. Dietterich
Has also published under the name of:
"Thomas Dietterich"
About the author:
No description available of Thomas G. Dietterich...
Publications by Thomas G. Dietterich (bibliography)
» 2009 «
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.”
Copyrights may apply
Shen, Jianqiang, Fitzhenry, Erin and Dietterich, Thomas G. (2009): Discovering frequent work procedures from resource connections. In: Proceedings of the 2009 International Conference on Intelligent User Interfaces 2009. pp. 277-286. Available online
“Intelligent desktop assistants could provide more help for users if they could learn models of the users' workflows. However, discovering desktop workflows is difficult because they unfold over extended periods of time (days or weeks) and they are interleaved with many other workflows because of user multi-tasking. This paper describes an approach to discovering desktop workflows based on rich instrumentation of information flow actions such as copy/paste, SaveAs, file copy, attach file to email message, and save attachment. These actions allow us to construct a graph whose nodes are files, email messages, and web pages and whose edges are these information flow actions. A class of workflows that we call work procedures can be discovered by applying graph mining algorithms to find frequent subgraphs. This paper describes an algorithm for mining frequent closed connected subgraphs and then describes the results of applying this method to data collected from a group of real users.”
Copyrights may apply
» 2007 «
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.”
Copyrights may apply
Shen, Jianqiang and Dietterich, Thomas G. (2007): Active EM to reduce noise in activity recognition. In: Proceedings of the 2007 International Conference on Intelligent User Interfaces 2007. pp. 132-140. Available online
“Intelligent desktop environments allow the desktop user to define a set of projects or activities that characterize the user's desktop work. These environments then attempt to identify the current activity of the user in order to provide various kinds of assistance. These systems take a hybrid approach in which they allow the user to declare their current activity but they also employ learned classifiers to predict the current activity to cover those cases where the user forgets to declare the current activity. The classifiers must be trained on the very noisy data obtained from the user's activity declarations. Instead of asking the user to review and relabel the data manually, we employ an active EM algorithm that combines the EM algorithm and active learning. EM can be viewed as retraining on its own predictions. To make it more robust, we only retrain on those predictions that are made with high confidence. For active learning, we make a small number of queries to the user based on the most uncertain instances. Experimental results on real users show this active EM algorithm can significantly improve the prediction precision, and that it performs better than either EM or active learning alone.”
Copyrights may apply
» 2006 «
Shen, Jianqiang, Li, Lida, Dietterich, Thomas G. and Herlocker, Jonathan L. (2006): A hybrid learning system for recognizing user tasks from desktop activities and email messages. In: Proceedings of the 2006 International Conference on Intelligent User Interfaces 2006. pp. 86-92. Available online
“The TaskTracer system seeks to help multi-tasking users manage the resources that they create and access while carrying out their work activities. It does this by associating with each user-defined activity the set of files, folders, email messages, contacts, and web pages that the user accesses when performing that activity. The initial TaskTracer system relies on the user to notify the system each time the user changes activities. However, this is burdensome, and users often forget to tell TaskTracer what activity they are working on. This paper introduces TaskPredictor, a machine learning system that attempts to predict the user's current activity. TaskPredictor has two components: one for general desktop activity and another specifically for email. TaskPredictor achieves high prediction precision by combining three techniques: (a) feature selection via mutual information, (b) classification based on a confidence threshold, and (c) a hybrid design in which a Naive Bayes classifier estimates the classification confidence but where the actual classification decision is made by a support vector machine. This paper provides experimental results on data collected from TaskTracer users.”
Copyrights may apply
Bao, Xinlong, Herlocker, Jonathan L. and Dietterich, Thomas G. (2006): Fewer clicks and less frustration: reducing the cost of reaching the right folder. In: Proceedings of the 2006 International Conference on Intelligent User Interfaces 2006. pp. 178-185. Available online
“Helping computer users rapidly locate files in their folder hierarchies has become an important research topic in today's intelligent user interface design. This paper reports on FolderPredictor, a software system that can reduce the cost of locating files in hierarchical folders. FolderPredictor applies a cost-sensitive prediction algorithm to the user's previous file access information to predict the next folder that will be accessed. Experimental results show that, on average, FolderPredictor reduces the cost of”
Copyrights may apply
SHOW THIS LIST ON YOUR HOMEPAGE
What do YOU think?
Give us your opinion! Do you have any comments/additions that you would like other visitors to see?
You say:
Mar 12th, 2010
Changes to this page (author)
16 Feb 2010: Enabled abstracts to be shown on Thomas G. Dietterich's author page.02 Jun 2009: Author was edited 02 Jun 2009: Author was edited
29 Oct 2008: Added a picture of Thomas G. Dietterich
28 Oct 2008: Added a picture of Thomas Dietterich
24 Jul 2007: Author was edited
24 Jul 2007: Author was added to the bibliography
24 Jul 2007: Author was edited
24 Jul 2007: Author was added to the bibliography
