Browsing by Author "Gleicher, Michael"

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    Boxer: Interactive Comparison of Classifier Results
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Gleicher, Michael; Barve, Aditya; Yu, Xinyi; Heimerl, Florian; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models. We present Boxer, a system to enable such comparison. Our system facilitates interactive exploration of the experimental results obtained by applying multiple classifiers to a common set of model inputs. The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. The system couples standard visual designs with set algebra interactions and comparative elements. This allows the user to compose and coordinate views to specify subsets and assess classifier performance on them. The flexibility of these compositions allow the user to address a wide range of scenarios in developing and assessing classifiers. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.
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    EuroVis 2019 CGF 38-3: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Gleicher, Michael; Viola, Ivan; Leitte, Heike; Gleicher, Michael and Viola, Ivan and Leitte, Heike
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    EuroVis 2020 CGF 39-3: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Gleicher, Michael; Viola, Ivan; Landesberger von Antburg, Tatiana; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
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    QUESTO: Interactive Construction of Objective Functions for Classification Tasks
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Das, Subhajit; Xu, Shenyu; Gleicher, Michael; Chang, Remco; Endert, Alex; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Building effective classifiers requires providing the modeling algorithms with information about the training data and modeling goals in order to create a model that makes proper tradeoffs. Machine learning algorithms allow for flexible specification of such meta-information through the design of the objective functions that they solve. However, such objective functions are hard for users to specify as they are a specific mathematical formulation of their intents. In this paper, we present an approach that allows users to generate objective functions for classification problems through an interactive visual interface. Our approach adopts a semantic interaction design in that user interactions over data elements in the visualization are translated into objective function terms. The generated objective functions are solved by a machine learning solver that provides candidate models, which can be inspected by the user, and used to suggest refinements to the specifications. We demonstrate a visual analytics system QUESTO for users to manipulate objective functions to define domain-specific constraints. Through a user study we show that QUESTO helps users create various objective functions that satisfy their goals.