{"total":20,"rows":[{"id":"2112.06409","title":"Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective","created":"2021-12-13","cited_by_count":116,"area":null},{"id":"2102.07750","title":"A Data Quality-Driven View of MLOps","created":"2021-02-15","cited_by_count":31,"area":null},{"id":"2304.01429","title":"Learning from data with structured missingness","created":"2023-04-04","cited_by_count":24,"area":null},{"id":"2204.11131","title":"Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines","created":"2022-04-23","cited_by_count":17,"area":null},{"id":"2302.04702","title":"REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines","created":"2023-02-09","cited_by_count":10,"area":null},{"id":"2308.10915","title":"DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data","created":"2023-08-20","cited_by_count":9,"area":null},{"id":"2310.01765","title":"Data Cleaning and Machine Learning: A Systematic Literature Review","created":"2023-10-03","cited_by_count":9,"area":null},{"id":"2106.05484","title":"A Unified Framework for Task-Driven Data Quality Management","created":"2021-06-10","cited_by_count":7,"area":null},{"id":"2407.12793","title":"Data Collection and Labeling Techniques for Machine Learning","created":"2024-06-19","cited_by_count":4,"area":null},{"id":"2410.21346","title":"Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach","created":"2024-10-28","cited_by_count":4,"area":null},{"id":"2201.04770","title":"Certifiable Robustness for Nearest Neighbor Classifiers","created":"2022-01-13","cited_by_count":3,"area":null},{"id":"2401.03359","title":"In-Database Data Imputation","created":"2024-01-07","cited_by_count":3,"area":null},{"id":"2303.04811","title":"Naive Bayes Classifiers over Missing Data: Decision and Poisoning","created":"2023-03-08","cited_by_count":2,"area":null},{"id":"2402.17926","title":"Certain and Approximately Certain Models for Statistical Learning","created":"2024-02-27","cited_by_count":1,"area":null},{"id":"2409.07510","title":"Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation","created":"2024-09-11","cited_by_count":1,"area":null},{"id":"2304.13636","title":"AutoCure: Automated Tabular Data Curation Technique for ML Pipelines","created":"2023-04-26","cited_by_count":0,"area":null},{"id":"2311.16185","title":"Enhancing Sentiment Analysis Results through Outlier Detection Optimization","created":"2023-11-25","cited_by_count":0,"area":null},{"id":"2405.18549","title":"Learning from Uncertain Data: From Possible Worlds to Possible Models","created":"2024-05-28","cited_by_count":0,"area":null},{"id":"2503.13921","title":"Learning Over Dirty Data with Minimal Repairs","created":"2025-03-18","cited_by_count":0,"area":null},{"id":"2602.01217","title":"Learning from Anonymized and Incomplete Tabular Data","created":"2026-02-01","cited_by_count":0,"area":null}],"next_cursor":null}