{"total":21,"rows":[{"id":"2209.09125","title":"Operationalizing Machine Learning: An Interview Study","created":"2022-09-16","cited_by_count":25,"area":null},{"id":"2411.10478","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","created":"2024-11-11","cited_by_count":19,"area":null},{"id":"2209.08018","title":"IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective","created":"2022-09-16","cited_by_count":16,"area":null},{"id":"2303.08485","title":"Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML","created":"2023-03-15","cited_by_count":16,"area":null},{"id":"2203.11085","title":"Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work","created":"2022-03-21","cited_by_count":15,"area":null},{"id":"2302.01448","title":"Out of Context: Investigating the Bias and Fairness Concerns of \"Artificial Intelligence as a Service\"","created":"2023-02-02","cited_by_count":15,"area":null},{"id":"2406.03348","title":"Position: A Call to Action for a Human-Centered AutoML Paradigm","created":"2024-06-05","cited_by_count":14,"area":null},{"id":"2106.15397","title":"Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines","created":"2021-06-26","cited_by_count":13,"area":null},{"id":"2405.03727","title":"Large Language Models Synergize with Automated Machine Learning","created":"2024-05-06","cited_by_count":9,"area":null},{"id":"2302.10827","title":"AutoML in The Wild: Obstacles, Workarounds, and Expectations","created":"2023-02-21","cited_by_count":8,"area":null},{"id":"2304.08366","title":"Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling","created":"2023-04-17","cited_by_count":7,"area":null},{"id":"2208.13116","title":"An Empirical Study on the Usage of Automated Machine Learning Tools","created":"2022-08-28","cited_by_count":4,"area":null},{"id":"2502.01276","title":"HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization","created":"2025-02-03","cited_by_count":4,"area":null},{"id":"2110.01108","title":"Human-Centered AI for Data Science: A Systematic Approach","created":"2021-10-03","cited_by_count":2,"area":null},{"id":"2307.10774","title":"Assessing the Use of AutoML for Data-Driven Software Engineering","created":"2023-07-20","cited_by_count":2,"area":null},{"id":"2501.14165","title":"LoCoML: A Framework for Real-World ML Inference Pipelines","created":"2025-01-24","cited_by_count":2,"area":null},{"id":"2203.01717","title":"Practitioner Motives to Use Different Hyperparameter Optimization Methods","created":"2022-03-03","cited_by_count":1,"area":null},{"id":"2503.04350","title":"EDCA - An Evolutionary Data-Centric AutoML Framework for Efficient Pipelines","created":"2025-03-06","cited_by_count":1,"area":null},{"id":"2307.16285","title":"Predicting delays in Indian lower courts using AutoML and Decision Forests","created":"2023-07-30","cited_by_count":0,"area":null},{"id":"2310.09314","title":"Eliciting Model Steering Interactions from Users via Data and Visual Design Probes","created":"2023-10-12","cited_by_count":0,"area":null},{"id":"2609.38552","title":"Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systems","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}