Gestalt: integrated support for implementation and analysis in machine learning

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 25,493 · see citing works

Data Mining

Witten, I. H. (2008) · Cited by 9,115 · see citing works

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 · see citing works

Readings in Information Visualization: Using Vision to Think

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Activity sensing in the wild

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 · see citing works

Applications of machine learning in cancer prediction and prognosis.

Cruz, J. A., & Wishart, D. S. (2007) · PubMed Central · Cited by 879 · see citing works

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 · see citing works

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 · see citing works

Interactive machine learning

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 · see citing works

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 · see citing works

Do Code Clones Matter

Juergens, E., Deissenboeck, F., Hummel, B., & Wagner, S. (2009) · Cited by 354 · see citing works

Do code clones matter?

Juergens, E., Deissenboeck, F., Hummel, B., & Wagner, S. (2009) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2009.5070547 · Cited by 318 · see citing works

Path-based faliure and evolution management

Chen, M. Y., Accardi, A., Kıcıman, E., Lloyd, J., Patterson, D., Fox, A., & Brewer, E. (2004) · Networked Systems Design and Implementation · Cited by 308 · see citing works

Wings: Intelligent Workflow-Based Design of Computational Experiments

Gil, Y., Ratnakar, V., Kim, J., Gonzalez-Calero, P. A., Groth, P., Moody, J., & Deelman, E. (2010) · IEEE Intelligent Systems · doi:10.1109/mis.2010.9 · Cited by 201 · see citing works

CueFlik

Fogarty, J. A., Tan, D. S., Kapoor, A., & Winder, S. A. J. (2008) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357061 · Cited by 178 · see citing works

MySong

Simon, I., Morris, D., & Basu, S. (2008) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/1357054.1357169 · Cited by 154 · see citing works

Authoring sensor-based interactions by demonstration with direct manipulation and pattern recognition

Hartmann, B., Abdulla, L., Mittal, M., & Klemmer, S. (2007) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1240624.1240646 · Cited by 154 · see citing works

Recursive restartability: turning the reboot sledgehammer into a scalpel

Candea, G., & Fox, A. (2005) · Workshop on Hot Topics in Operating Systems · doi:10.1109/hotos.2001.990072 · Cited by 139 · see citing works

Investigating statistical machine learning as a tool for software development

Patel, K., Fogarty, J. A., Landay, J. A., & Harrison, B. L. (2008) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357160 · Cited by 131 · see citing works

Eyepatch

Maynes-Aminzade, D., Winograd, T. A., & Igarashi, T. (2007) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1294211.1294219 · Cited by 53 · see citing works

An 'object-use fingerprint': the use of electronic sensors for human identification

Hodges, M. R., & Pollack, M. E. (2007) · doi:10.5555/1771592.1771609 · Cited by 39 · see citing works

The LabVIEW Style Book

Blume, P. A. (2007) · Cited by 36 · see citing works

Visual interfaces to data

Stolte, C. (2010) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1807167.1807287 · Cited by 9 · see citing works

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works

Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda

Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174156 · Cited by 743 · see citing works

"Why Should I Trust You?": Explaining the Predictions of Any ClassifierPreprint

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04938 · Cited by 587 · see citing works

Deep learning in the construction industry: A review of present status and future innovations

Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A., Delgado, J. M. D., Akinadé, O. O., & Ahmed, A. A. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.101827 · Cited by 544 · see citing works

Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models

Krause, J., Perer, A., & Ng, K. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858529 · Cited by 390 · see citing works

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models

Kahng, M., Andrews, P., Kalro, A., & Chau, D. H. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744718 · Cited by 349 · see citing works

ModelTracker

Amershi, S., Chickering, M., Drucker, S. M., Lee, B., Simard, P., & Suh, J. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702509 · Cited by 263 · see citing works

Squares: Supporting Interactive Performance Analysis for Multiclass Classifiers

Ren, D., Amershi, S., Lee, B., Suh, J., & Williams, J. D. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2598828 · Cited by 200 · see citing works

Do Convolutional Neural Networks Learn Class Hierarchy?

Alsallakh, B., Jourabloo, A., Ye, M., Liu, X., & Ren, L. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744683 · Cited by 199 · see citing works

Scaling big data mining infrastructure

Lin, J., & Ryaboy, D. V. (2013) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2481244.2481247 · Cited by 164 · see citing works

Grounding Interactive Machine Learning Tool Design in How Non-Experts Actually Build Models

Yang, Q., Suh, J., Chen, N.-C., & Ramos, G. (2018) · Proceedings of the 2018 Designing Interactive Systems Conference · doi:10.1145/3196709.3196729 · Cited by 156 · see citing works

Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences

Shankar, S., Zamfirescu-Pereira, J. D., Hartmann, B., Parameswaran, A., & Arawjo, I. (2024) · Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3654777.3676450 · Cited by 151 · see citing works

Human-XAI Interaction: A Review and Design Principles for Explanation User Interfaces

Chromik, M., & Butz, A. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85616-8_36 · Cited by 128 · see citing works

Interactive machine teaching: a human-centered approach to building machine-learned models

Ramos, G., Meek, C., Simard, P., Suh, J., & Ghorashi, S. (2020) · Human-Computer Interaction · doi:10.1080/07370024.2020.1734931 · Cited by 120 · see citing works

Flock

Cheng, J., & Bernstein, M. S. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675214 · Cited by 112 · see citing works

Iris

Fast, E., Chen, B., Mendelsohn, J., Bassen, J., & Bernstein, M. S. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174047 · Cited by 110 · see citing works

Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

Xin, D., Wu, E. Y., Lee, D. J.-L., Salehi, N., & Parameswaran, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445306 · Cited by 110 · see citing works

Visual Diagnosis of Tree Boosting Methods

Liu, S., Xiao, J., Liu, J., Wang, X., Wu, J., & Zhu, J. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744378 · Cited by 97 · see citing works

Machine Learning Education for Artists, Musicians, and Other Creative Practitioners

Fiebrink, R. A. (2019) · ACM Transactions on Computing Education · doi:10.1145/3294008 · Cited by 95 · see citing works

Interactive and interpretable machine learning models for human machine collaboration

Kim, B. (2015) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 83 · see citing works

Visual exploration of machine learning results using data cube analysis

Kahng, M., Fang, D., & Chau, D. H. (2016) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/2939502.2939503 · Cited by 78 · see citing works

The Gesture Recognition Toolkit

Gillian, N. E., & Paradiso, J. A. (2017) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-57021-1_17 · Cited by 77 · see citing works

mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks

Kery, M. B., Ren, D., Hohman, F., Moritz, D., Wongsuphasawat, K., & Patel, K. (2020) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3379337.3415842 · Cited by 71 · see citing works

Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels

Görtler, J., Hohman, F., Moritz, D., Wongsuphasawat, K., Ren, D., Nair, R. S., Kirchner, M., & Patel, K. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501823 · Cited by 69 · see citing works

On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems

Nushi, B., Kamar, E. S., Horvitz, E., & Kossmann, D. (2017) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v31i1.10633 · Cited by 65 · see citing works

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Heger, A., Marquis, L. B., Vorvoreanu, M., Wallach, H., & Vaughan, J. W. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555760 · Cited by 60 · see citing works

Understanding the Role of Alternatives in Data Analysis Practices

Liu, J., Boukhelifa, N., & Eagan, J. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934593 · Cited by 58 · see citing works

Gestures à Go Go

Leiva, L. A., Martín-Albo, D., & Plamondon, R. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2799648 · Cited by 56 · see citing works

Understanding the Role of Interactive Machine Learning in Movement Interaction Design

Gillies, M. F. P. (2019) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3287307 · Cited by 53 · see citing works

UMLAUT: Debugging Deep Learning Programs using Program Structure and Model Behavior

Schoop, E., Huang, F., & Hartmann, B. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445538 · Cited by 52 · see citing works

DeepCompare: Visual and Interactive Comparison of Deep Learning Model Performance

Murugesan, S., Malik, S., Du, F., Koh, E., & Lai, T. (2019) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2019.2919033 · Cited by 49 · see citing works

HELIX

Xin, D., Macke, S., Ma, L., Liu, J., Song, S., & Parameswaran, A. (2018) · Proceedings of the VLDB Endowment · doi:10.14778/3297753.3297763 · Cited by 48 · see citing works

Symphony: Composing Interactive Interfaces for Machine Learning

Bäuerle, A., Cabrera, Á. A., Hohman, F., Maher, M., Koski, D., Suau, X., Barik, T., & Moritz, D. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502102 · Cited by 50 · see citing works

Researching AI Legibility through Design

Lindley, J., Akmal, H. A., Pilling, F. L., & Coulton, P. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376792 · Cited by 45 · see citing works

A User‐based Visual Analytics Workflow for Exploratory Model Analysis

Cashman, D., Humayoun, S. R., Heimerl, F., Park, K., Das, S., Thompson, J., Saket, B., Mosca, A., Stasko, J., Endert, A., et al. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13681 · Cited by 41 · see citing works

DejaVu

Kato, J., McDirmid, S., & Cao, X. G. (2012) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2380116.2380142 · Cited by 38 · see citing works

Software Developers Learning Machine Learning: Motivations, Hurdles, and Desires

Cai, C. J., & Guo, P. J. (2019) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2019.8818751 · Cited by 37 · see citing works

Interactive Debugging and Steering of Multi-Agent AI Systems

Epperson, W., Bansal, G., Dibia, V. C., Fourney, A., Gerrits, J., Zhu, E. (., & Amershi, S. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713581 · Cited by 35 · see citing works

Evolving the Ecosystem of Personal Behavioral Data

Wiese, J., Das, S., Hong, J., & Zimmerman, J. (2017) · Human-Computer Interaction · doi:10.1080/07370024.2017.1295857 · Cited by 35 · see citing works

Motion-Sound Mapping through Interaction

Françoise, J., & Bevilacqua, F. P. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3211826 · Cited by 35 · see citing works

Untitled work

(2015) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 34 · see citing works

Gesture script

Lü, H., Fogarty, J. A., & Li, Y. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557263 · Cited by 33 · see citing works

Using Multiple Models to Understand Data

Patel, K., Drucker, S. M., Fogarty, J. A., Kapoor, A., & Tan, D. S. (2012) · Cited by 32 · see citing works

Label-and-Learn

Sun, Y., Lank, E., & Terry, M. A. (2017) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3025171.3025208 · Cited by 31 · see citing works

SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-grained Finger Microgestures

Sharma, A., Salchow-Hömmen, C., Mollyn, V., Nittala, A. S., Hedderich, M. A., Koelle, M., Seel, T., & Steimle, J. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3569894 · Cited by 29 · see citing works

Using Expert Patterns in Assisted Interactive Machine Learning: A Study in Machine Teaching

Wall, E., Ghorashi, S., & Ramos, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29387-1_34 · Cited by 28 · see citing works

Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design

Lam, M. S., Ma, Z., Li, A., Freitas, I., Wang, D., Landay, J. A., & Bernstein, M. S. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581290 · Cited by 25 · see citing works

CrowdLearner

Amini, S., & Li, Y. (2013) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2501988.2502029 · Cited by 24 · see citing works

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network ModelsPreprint

Kahng, M., Andrews, P., Kalro, A., & Chau, D. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01942 · Cited by 21 · see citing works

Characterizing Practices, Limitations, and Opportunities Related to Text Information Extraction Workflows: A Human-in-the-loop Perspective

Rahman, S., & Kandogan, E. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502068 · Cited by 20 · see citing works

Interactive Machine Learning for End-User Innovation

Bernardo, F., Zbyszyński, M., Fiebrink, R. A., & Grierson, M. (2016) · Goldsmiths (University of London) · Cited by 20 · see citing works

Making data analysis expertise broadly accessible through workflows

Hauder, M., Gil, Y., Sethi, R. J., Liu, Y., & Jo, H. (2011) · Workshop on Workflows in Support of Large-Scale Science (WORKS) · doi:10.1145/2110497.2110507 · Cited by 19 · see citing works

Making machine learning useable

Zhou, J. J., & Chen, F. (2015) · International Journal of Intelligent Systems Technologies and Applications · doi:10.1504/ijista.2015.074069 · Cited by 19 · see citing works

Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training

Yu, G. X., Grossman, T., & Pekhimenko, G. (2020) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3379337.3415890 · Cited by 19 · see citing works

A study on interpretability of decision of machine learning

Shirataki, S., & Yamaguchi, S. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258557 · Cited by 19 · see citing works

Lowering the barrier to applying machine learning

Patel, K. (2010) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1866218.1866222 · Cited by 18 · see citing works

Crowdsourcing – A Step Towards Advanced Machine Learning

Abhigna, B. S., Soni, N., & Dixit, S. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.062 · Cited by 18 · see citing works

Iris: A Conversational Agent for Complex TasksPreprint

Fast, E., Chen, B., Mendelsohn, J., Bassen, J., & Bernstein, M. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.05015 · Cited by 16 · see citing works

Making Machine-Learning Applications for Time-Series Sensor Data Graphical and Interactive

Kim, S., Tasse, D., & Dey, A. K. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2983924 · Cited by 15 · see citing works

FAIRVIS: Visual Analytics for Discovering Intersectional Bias in Machine Learning

Cabrera, Á. A., Epperson, W., Hohman, F., Kahng, M., Morgenstern, J., & Chau, D. H. (2019) · 2019 IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast47406.2019.8986948 · Cited by 14 · see citing works

Programming with Examples to Develop Data-Intensive User Interfaces

Kato, J., Igarashi, T., & Goto, M. (2016) · Computer · doi:10.1109/mc.2016.217 · Cited by 13 · see citing works

Supporting Data Workers To Perform Exploratory Programming

Subramanian, K., Zubarev, I., Völker, S., & Borchers, J. (2019) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3290607.3313027 · Cited by 12 · see citing works

Picode

Kato, J., Sakamoto, D., & Igarashi, T. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2466422 · Cited by 12 · see citing works

Machine Learning for Makers

Mellis, D. A., Zhang, B., Leung, A. S., & Hartmann, B. (2017) · Designing Interactive Systems Conference · doi:10.1145/3064663.3064735 · Cited by 12 · see citing works

Agile Modeling: From Concept to Classifier in Minutes

Stretcu, O., Vendrow, E. B., Hata, K., Viswanathan, K., Ferrari, V., Tavakkol, S., Zhou, W., Avinash, A., Luo, E., Alldrin, N., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02040 · Cited by 11 · see citing works

User-Centred Design Actions for Lightweight Evaluation of an Interactive Machine Learning Toolkit

Bernardo, F., Grierson, M., & Fiebrink, R. A. (2018) · Journal of Science and Technology of the Arts · doi:10.7559/citarj.v10i2.509 · Cited by 11 · see citing works

Faulty or Ready? Handling Failures in Deep-Learning Computer Vision Models until Deployment: A Study of Practices, Challenges, and Needs

Balayn, A., Rikalo, N., Yang, J., & Bozzon, A. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581555 · Cited by 10 · see citing works

Eye into AI: Evaluating the Interpretability of Explainable AI Techniques through a Game with a Purpose

Morrison, K., Jain, M., Hammer, J., & Perer, A. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610064 · Cited by 10 · see citing works

Evaluating the Effect of Feedback from Different Computer Vision Processing Stages

Kittley-Davies, J., Alqaraawi, A., Yang, R., Costanza, E., Rogers, A., & Stein, S. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300273 · Cited by 10 · see citing works

PICL

Fourney, A., & Terry, M. A. (2012) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2380116.2380188 · Cited by 8 · see citing works

I can do text analytics!

Yang, H., Wickham, D. P., Chiticariu, L., Li, Y., Nguyen, B., & Carreno-Fuentes, A. (2013) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2470654.2466212 · Cited by 7 · see citing works

VisionSketch: Integrated Support for Example-Centric Programming of Image Processing Applications

Kato, J., & Igarashi, T. (2020) · A K Peters/CRC Press eBooks · doi:10.1201/9781003059325-15 · Cited by 7 · see citing works

ODEN: Live Programming for Neural Network Architecture Editing

Zhao, C., Shen, I., Fukusato, T., Kato, J., & Igarashi, T. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511120 · Cited by 7 · see citing works

QUESTO: Interactive Construction of Objective Functions for Classification Tasks

Das, S., Xu, S., Gleicher, M. L., Chang, R., & Endert, A. (2020) · Computer Graphics Forum · doi:10.1111/cgf.13970 · Cited by 7 · see citing works

Helix: Holistic Optimization for Accelerating Iterative Machine LearningPreprint

Xin, D., Macke, S., Ma, L., Liu, J., Song, S., & Parameswaran, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05762 · Cited by 7 · see citing works

EvalAssist: Insights on Task-Specific Evaluations and AI-Assisted Judgment Strategy Preferences

Ashktorab, Z., Desmond, M. J., Pan, Q., Johnson, J. M., Cooper, M. S., Daly, E., Nair, R., Pedapati, T., Do, H. J., & Geyer, W. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747740 · Cited by 6 · see citing works

OpenVL: A task-based abstraction for developer-friendly computer vision

Miller, G., & Fels, S. (2013) · Proceedings · doi:10.1109/wacv.2013.6475031 · Cited by 6 · see citing works

Lowering the barrier to applying machine learning

Patel, K. (2010) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1753846.1753882 · Cited by 6 · see citing works

SCRAM: Simple Checks for Realtime Analysis of Model Training for Non-Expert ML Programmers

Schoop, E., Huang, F., & Hartmann, B. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3382879 · Cited by 6 · see citing works

Mallard: Turn the Web into a Contextualized Prototyping Environment for Machine Learning

Zhang, X., & Guo, P. J. (2019) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3332165.3347936 · Cited by 5 · see citing works

SparseEMG: Computational Design of Sparse EMG Layouts for Sensing Gestures

Kumar, A., Irudayaraj, A. A. R., Chandra, I., Sharma, A., & Nittala, A. S. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747614 · Cited by 4 · see citing works

VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-Making

Antar, A. D., Molaei, S., Chen, Y.-Y., Lee, M. L., & Banovic, N. (2024) · ACM Symposium on User Interface Software and Technology · doi:10.1145/3654777.3676323 · Cited by 8 · see citing works

Developer-centred interface design for computer vision

Miller, G., & Fels, S. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130274 · Cited by 3 · see citing works

TextAlive Online: Live Programming of Kinetic Typography Videos with Online Music

Kato, J., Nakano, T., & Goto, M. (2015) · doi:10.5281/zenodo.19355 · Cited by 3 · see citing works

Integrated visual representations for programming with real-world input and output

Kato, J. (2013) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2508468.2508476 · Cited by 3 · see citing works

StatWire

Subramanian, K., Maas, J., Ellers, M., Wacharamanotham, C., Voelker, S., & Borchers, J. (2018) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3170427.3188528 · Cited by 3 · see citing works

ML Tools for the Web: A Way for Rapid Prototyping and HCI Research

Li, N., Mayes, J., & Yu, P. (2021) · Human-computer interaction series · doi:10.1007/978-3-030-82681-9_10 · Cited by 3 · see citing works

Novice-Centric Visualizations for Machine Learning

Sun, Y. (2016) · UWSpace (University of Waterloo) · Cited by 3 · see citing works

HClass: Automatic classification tool for health pathologies using artificial intelligence techniques

Garcia-Chimeno, Y., & García-Zapirain, B. (2015) · Bio-Medical Materials and Engineering · doi:10.3233/bme-151484 · Cited by 2 · see citing works

Developer toolchains for large-scale analytics: Two case studies

Rosenthal, S. L., McMillan, S., & Gaston, M. E. (2015) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2015.7363887 · Cited by 2 · see citing works

The Role of Expertise on Insight Generation from Visualization Sequences

Rosenthal, S. L., & Chung, T. R. (2022) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc53370.2022.9833097 · Cited by 2 · see citing works

DSWorkFlow: A Framework for Capturing Data Scientists’ Workflows

Mash, M., Rosenthal, S. L., & Simmons, R. G. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451683 · Cited by 2 · see citing works

On the Feasibility of Automatically Describing n-dimensional Objects

Duboue, P. A. (2013) · Cited by 1 · see citing works

Understanding Interactive and Explainable Feedback for Supporting Non- Experts with Data Preparation for Building a Deep Learning Model

Kim, Y., Lee, K., & Oh, U. (2020) · International journal of advanced smart convergence · doi:10.7236/ijasc.2020.9.2.90 · Cited by 1 · see citing works

Explainable Machine Learning and Visual Knowledge Discovery

Kovalerchuk, B. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_39 · Cited by 1 · see citing works

Slide to Explore 'What If': An Analysis of Explainable Interfaces

Meyer, L. S., & Zhu, J. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3677045.3685416 · Cited by 1 · see citing works

Weakly Supervised Learning by a Confusion Matrix of Contexts

Wu, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26142-9_6 · Cited by 1 · see citing works

Do Machine Teachers Dream of Algorithms

Ramos, G., Ng, F., Sultanum, N., Meek, C. E., Suh, J., & Ghorashi, S. (2019) · Cited by 1 · see citing works

Accelerating Entrepreneurial Decision-Making Through Hybrid IntelligencePreprint

Dellermann, D. (2020) · arXiv (Cornell University) · doi:10.17170/kobra-202004301196 · Cited by 2 · see citing works

Towards Evaluating Exploratory Model Building Process with AutoML SystemsPreprint

Hong, S.-S., Castelo, S., D'Orazio, V. J., Benthune, C., Santos, A. S. R., Langevin, S., Jonker, D., Bertini, E., & Freire, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.00449 · Cited by 1 · see citing works

SEE PROFILE

Bancroft, N., Drucker, S. M., Ko, A. J., & Landay, J. A. (2016)

Gestalt: Integrated Support for Implementation and Analysis in Machine Learning

Patel, K., Bancroft, N., Drucker, S. M., Fogarty, J. A., Ko, A. J., & Landay, J. A. (2010)

Lowering the barrier to applying machine learning

Patel, K. (2010) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1866218.1866222 · Cited by 18 · see citing works

A Framework for an Automated Development Environment to Support the Data-driven Machine Learning Paradigm

Bowman, A. D., Prabhakar, S. P., & Jololian, L. (2022) · IEEE SoutheastCon · doi:10.1109/southeastcon48659.2022.9764094 · Cited by 1 · see citing works

Label-and-Learn

Sun, Y., Lank, E., & Terry, M. A. (2017) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3025171.3025208 · Cited by 31 · see citing works