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 25,543

Data Mining

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

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

Applications 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

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

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

"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

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

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

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv

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

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

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

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 270

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 202

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 214

Scaling big data mining infrastructure

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

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

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

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 147

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 134

Flock

Cheng, J., & Bernstein, M. S. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675214 · Cited by 117

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

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

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

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 107

Interactive and interpretable machine learning models for human machine collaboration

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

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

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

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

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

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

Nushi, B., Kamar, E. S., Horvitz, E., & Kossmann, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10633 · Cited by 38

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

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

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

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 60

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 58

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

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 54

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 54

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 49

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

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

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 39

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

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 36

Motion-Sound Mapping through Interaction

Françoise, J., & Bevilacqua, F. P. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3211826 · Cited by 36

Untitled work

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

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

Using Multiple Models to Understand Data

Patel, K., Drucker, S. M., Fogarty, J. A., Kapoor, A., & Tan, D. S. (2012) · Cited by 31

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 34

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

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 30

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 31

CrowdLearner

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

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

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 22

Interactive Machine Learning for End-User Innovation

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

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

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 20

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

Yu, G. X., Grossman, T., & Pekhimenko, G. G. (2020) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3379337.3415890 · Cited by 21

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

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 17

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 22

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

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 16

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

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

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

Picode

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

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

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 13

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 13

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 13

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

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

PICL

Fourney, A., & Terry, M. A. (2012) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2380116.2380188 · Cited by 9

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

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

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 8

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

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 15

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

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

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

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

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 6

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

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., & Banović, N. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676323 · Cited by 9

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

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

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

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

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 4

Novice-Centric Visualizations for Machine Learning

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

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

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

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

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

On the Feasibility of Automatically Describing n-dimensional Objects

Duboue, P. A. (2013) · Cited by 1

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

Explainable Machine Learning and Visual Knowledge Discovery

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

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

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

Do Machine Teachers Dream of Algorithms

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

Accelerating Entrepreneurial Decision-Making Through Hybrid IntelligencePreprint

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

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 6

SEE PROFILE

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

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 17

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

Label-and-Learn

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A classification and review of tools for developing and interacting with machine learning systems

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