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Ashish Kapoor

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Publications by Ashish Kapoor (bibliography)

» 2009 «

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Talbot, Justin, Lee, Bongshin, Kapoor, Ashish and Tan, Desney S. (2009): EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 1283-1292. Available online

“Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative. In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components. In this paper, we present EnsembleMatrix, an interactive visualization system that presents a graphical view of confusion matrices to help users understand relative merits of various classifiers. EnsembleMatrix allows users to directly interact with the visualizations in order to explore and build combination models. We evaluate the efficacy of the system and the approach in a user study. Results show that users are able to quickly combine multiple classifiers operating on multiple feature sets to produce an ensemble classifier with accuracy that approaches best-reported performance classifying images in the CalTech-101 dataset.”

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» 2008 «

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Fogarty, James, Tan, Desney S., Kapoor, Ashish and Winder, Simon (2008): CueFlik: interactive concept learning in image search. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 29-38. Available online

“Web image search is difficult in part because a handful of keywords are generally insufficient for characterizing the visual properties of an image. Popular engines have begun to provide tags based on simple characteristics of images (such as tags for black and white images or images that contain a face), but such approaches are limited by the fact that it is unclear what tags end users want to be able to use in examining Web image search results. This paper presents CueFlik, a Web image search application that allows end users to quickly create their own rules for re ranking images based on their visual characteristics. End users can then re rank any future Web image search results according to their rule. In an experiment we present in this paper, end users quickly create effective rules for such concepts as "product photos", "portraits of people", and "clipart". When asked to conceive of and create their own rules, participants create such rules as "sports action shot" with images from queries for "basketball" and "football". CueFlik represents both a promising new approach to Web image search and an important study in end user interactive machine learning.”

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Kapoor, Ashish and Horvitz, Eric (2008): Experience sampling for building predictive user models: a comparative study. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 657-666. Available online

“Experience sampling has been employed for decades to collect assessments of subjects' intentions, needs, and affective states. In recent years, investigators have employed automated experience sampling to collect data to build predictive user models. To date, most procedures have relied on random sampling or simple heuristics. We perform a comparative analysis of several automated strategies for guiding experience sampling, spanning a spectrum of sophistication, from a random sampling procedure to increasingly sophisticated active learning. The more sophisticated methods take a decision-theoretic approach, centering on the computation of the expected value of information of a probe, weighing the cost of the short-term disruptiveness of probes with their benefits in enhancing the long-term performance of predictive models. We test the different approaches in a field study, focused on the task of learning predictive models of the cost of interruption.”

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» 2007 «

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Kapoor, Ashish, Burleson, Winslow and Picard, Rosalind W. (2007): Automatic prediction of frustration. In International Journal of Human-Computer Studies, 65 (8) pp. 724-736

“Predicting when a person might be frustrated can provide an intelligent system with important information about when to initiate interaction. For example, an automated Learning Companion or Intelligent Tutoring System might use this information to intervene, providing support to the learner who is likely to otherwise quit, while leaving engaged learners free to discover things without interruption. This paper presents the first automated method that assesses, using multiple channels of affect-related information, whether a learner is about to click on a button saying "I'm frustrated." The new method was tested on data gathered from 24 participants using an automated Learning Companion. Their indication of frustration was automatically predicted from the collected data with 79% accuracy (chance=58%). The new assessment method is based on Gaussian process classification and Bayesian inference. Its performance suggests that non-verbal channels carrying affective cues can help provide important information to a system for formulating a more intelligent response.”

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Used on the following page:

» Multimodal Affective Computing: [/encyclopedia/multimodal_affective_computing.html]

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Kapoor, Ashish and Horvitz, Eric (2007): Principles of Lifelong Learning for Predictive User Modeling. In: Conati, Cristina, McCoy, Kathleen F. and Paliouras, Georgios (eds.) User Modeling 2007 - 11th International Conference - UM 2007 June 25-29, 2007, Corfu, Greece. pp. 37-46. Available online

» 2005 «

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Kapoor, Ashish and Picard, Rosalind W. (2005): Multimodal affect recognition in learning environments. In: Zhang, Hongjiang, Chua, Tat-Seng, Steinmetz, Ralf, Kankanhalli, Mohan S. and Wilcox, Lynn (eds.) Proceedings of the 13th ACM International Conference on Multimedia November 6-11, 2005, Singapore. pp. 677-682. Available online

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Changes to this page (author)

25 Feb 2010: Enabled abstracts to be shown on Ashish Kapoor's author page.
17 Jun 2009: Author was edited

30 May 2009: Author was edited
09 May 2009: Author was edited
12 May 2008: Author was edited
12 May 2008: Author was edited
12 May 2008: Author was edited
04 Feb 2008: Author was added to the bibliography

Publication statistics

Publication period:2005-2009
Publication count:6
Number of co-authors:8

Productive colleagues

Ashish Kapoor's 3 most productive colleagues in number of publications:

Eric Horvitz:44
Rosalind W. Picard:35
Desney S. Tan:31

Collaboration count

Number of publications with 3 favourite co-authors:

Eric Horvitz:2
Desney S. Tan:2
Rosalind W. Picard:2

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Learn more about Ashish Kapoor:
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Conference Calendar

Mar 22

Workshop on Pervasive healthcare in the home : Supporting patient motivation and engagement

Mar 22

UX People

Mar 22

ETRA '10 - Eye Tracking Research & Applications Symp.

Mar 21

“ Software design is the act of determining the user's experience with a piece of software. It has nothing to do with how the code works inside, or how big or small the code is. The designer's task is to specify completely and unambiguously the user's whole experience. ”

-- David Liddle, From Bringing Design to Software, edited by Terry Winograd, 1996 ”

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Eva Hornecker on Tangible Interaction

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Ashish Kapoor

Publications by Ashish Kapoor (bibliography)

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