A dataset revealing high inter-designer disagreement on UI preferences motivates a sample-efficient method that personalizes generative interfaces by embedding new users in the space of prior designers, outperforming baselines in both modeling and user preference.
Mixed citations
Nguyen, Hoda Heidari, and Jana Schaich Borg
Mixed citation behavior. Most common role is background (60%).
7
Pith papers citing it
Background
60% of classified citations
citation-role summary
background 5
citation-polarity summary
years
2026 7verdicts
UNVERDICTED 7roles
background 5representative citing papers
A survey of 457 papers yields a six-dimensional design space for abstraction in interactive systems that reframes gulfs of execution and evaluation while articulating cognitive and design processes for bridging abstraction gaps.
Users adapt existing workflow patterns to create custom features in an AI email system via conversation, turning the inbox into a user-shaped flexible data layer while managing risks like mis-specified behavior through ongoing oversight.
Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.
AnimationDiff is a visual comparison tool that combines contextual scene viewing, overlay/side-by-side modes, filtering, and temporal lenses to help users select among generated 3D character animations.
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
Barriers like non-adaptable data formats, legacy security, and cognitive skill gaps hinder GenUI adoption, requiring new scientific methods for evaluation and usage tracking.
citing papers explorer
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Efficient Personalization of Generative User Interfaces
A dataset revealing high inter-designer disagreement on UI preferences motivates a sample-efficient method that personalizes generative interfaces by embedding new users in the space of prior designers, outperforming baselines in both modeling and user preference.
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Making Abstraction Concrete: A Design Space and Interaction Model of Abstraction in Interactive Systems
A survey of 457 papers yields a six-dimensional design space for abstraction in interactive systems that reframes gulfs of execution and evaluation while articulating cognitive and design processes for bridging abstraction gaps.
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Conversational Customization of Productivity Systems: A Design Probe of Malleable AI Interfaces
Users adapt existing workflow patterns to create custom features in an AI email system via conversation, turning the inbox into a user-shaped flexible data layer while managing risks like mis-specified behavior through ongoing oversight.
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Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences
Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.
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AnimationDiff: A Visual Comparison Tool for Generated 3D Character Animations
AnimationDiff is a visual comparison tool that combines contextual scene viewing, overlay/side-by-side modes, filtering, and temporal lenses to help users select among generated 3D character animations.
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The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
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Hidden Technical Debt in Generative (GenUI) and Malleable User Interfaces
Barriers like non-adaptable data formats, legacy security, and cognitive skill gaps hinder GenUI adoption, requiring new scientific methods for evaluation and usage tracking.