The DOI matches its Crossref record; 23 of the 24 references it cites were found in a registry or an index.
Gestalt: integrated support for implementation and analysis in machine learning
Abstract
We present Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, we explicitly support both code and data. Gestalt allows developers to implement a classification pipeline, analyze data as it moves through that pipeline, and easily transition between implementation and analysis. An experiment shows this significantly improves the ability of developers to find and fix bugs in machine learning systems. Our discussion of Gestalt and our experimental observations provide new insight into general-purpose support for the machine learning process.
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“Interfaces that facilitate seamless transitions between these activities result in fewer errors and outputs that better match users' expectations [38, 46].”
From Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences (2024) · cites this paper as [38]
Citations
Cites
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Witten, I. H. (2008) · Cited by 9,143
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
Readings in Information Visualization: Using Vision to Think
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999) · Cited by 3,935
Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y., Froehlich, J. E., Harrison, B. L., Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., et al. (2008) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357335 · Cited by 1,133
Self-citationApplications of machine learning in cancer prediction and prognosis.
Cruz, J. A., & Wishart, D. S. (2007) · PubMed Central · Cited by 879
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Wobbrock, J. O., Wilson, A. D., & Li, Y. (2007) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1294211.1294238 · Cited by 823
Specifying gestures by example
Rubine, D. (1991) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/122718.122753 · Cited by 780
Fails, J. A., & Olsen, D. R. (2003) · Proceedings of the 8th international conference on Intelligent user interfaces · doi:10.1145/604045.604056 · Cited by 633
Data mining and machine learning in astronomy
BALL, N. M., & BRUNNER, R. J. (2010) · International Journal of Modern Physics D · doi:10.1142/s0218271810017160 · Cited by 383
Research works printed but not linked: 1
- 10.5555/1251175.1251198 · DOI
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Items 101–111 of 111
OpenVL: Abstracting Vision Tasks Using a Segment-Based Language Model
Miller, G., Oldridge, S., & Fels, S. (2013) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2013.55
Exploring Electronic Storyboards as Interdisciplinary Design Tools for Pervasive Computing
Forsyth, J. B. (2015) · VTechWorks (Virginia Tech)
Stroke Gesture Synthesis in Human-Computer Interaction
Leiva, L. A., Martín-Albo, D., Vatavu, R.-D., & Réjean Plamondon, B. (2020) · Series in machine perception and artificial intelligence · doi:10.1142/9789811226830_0010
CACTUS: Detecting and Resolving Conflicts in Objective FunctionsPreprint
Das, S., & Endert, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07805
OpenVL: An Abstraction for Developer-Friendly Computer Vision
Miller, G., & Fels, S. (2013)
Mehler, A., Hemati, W., Gleim, R., & Baumartz, D. (2018) · De Gruyter eBooks · doi:10.1515/9783110538663-008
Lobin, H., Schneider, R., & Witt, A. (2018) · De Gruyter eBooks · doi:10.1515/9783110538663-203
Motion-sound Mapping By Demonstration
Françoise, J. (2015) · theses.fr (ABES) · doi:10.70675/bd9c2efaz9c5fz4a69z9decz87cf14835977 · Cited by 16
RAG Without the Lag: Enabling "What-If" Analysis for Retrieval-Augmented Generation Pipelines
Lauro, Q. R., Shankar, S., Zeighami, S., & Parameswaran, A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790874 · Cited by 9
Guo, M., Gotz, D., & Wang, Y. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806512
Metadata
- Authors
- Patel, K., Bancroft, N., Drucker, S. M., Fogarty, J., Ko, A. J., & Landay, J.
- Affiliations
- University of Washington (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 23nd annual ACM symposium on User interface software and technology
- Publisher
- ACM
- Published
- 2010-10-03
- Pages
- 37-46
- OpenAlex
- W2165254944
- Cited by
- 112
- References
- 24
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 5 · 2022: 7 · 2021: 7 · 2020: 12 · 2019: 14 · 2018: 11 · 2017: 12 · 2016: 8 · 2015: 11 · 2014: 1 · 2013: 9 · 2012: 3
- FWCI
- 3.59
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Symposium on User Interface Software and Technology (UIST) (publishedVersion)
- Funders
- National Science Foundation
- National Defense Science and Engineering Graduate
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- Gestalt, machine learning pipelines, classification pipeline, bug fixes
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