ABSTRACT
The 2nd International Workshop on Data Quality Assessment for Machine Learning (DQAML'21) is organized in conjunction with the Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD). This workshop aims to serve as a forum for the presentation of research related to data quality assessment and remediation in AI/ML pipeline. Data quality is a critical issue in the data preparation phase and involves numerous challenging problems related to detection, remediation, visualization and evaluation of data issues. The workshop aims to provide a platform to researchers and practitioners to discuss such challenges across different modalities of data like structured, time series, text and graphical. The aim is to attract perspectives from both industrial and academic circles.
- Bortik Bandyopadhyay, Sambaran Bandyopadhyay, Srikanta Bedathur, Nitin Gupta, Sameep Mehta, Shashank Mujumdar, Srinivasan Parthasarathy, and Hima Patel. 2021. 1st International Workshop on Data Assessment and Readiness for AI. In PAKDD (Workshops).Google Scholar
- Abhinav Jain, Hima Patel, Lokesh Nagalapatti, Nitin Gupta, Sameep Mehta, Shanmukha Guttula, Shashank Mujumdar, Shazia Afzal, Ruhi Sharma Mittal, and Vitobha Munigala. 2020. Overview and Importance of Data Quality for Machine Learning Tasks. In KDD.Google Scholar
- Hima Patel, Nitin Gupta, Sameep Mehta, Shanmukha Guttula, Shashank Mujumdar, Shazia Afzal, Ruhi Sharma Mittal, Vitobha Munigala, Naveen Panwar, Sambaran Bandyopadhyay, and Satoshi Musda. 2021. Data Quality for Machine Learning Tasks. In KDD.Google Scholar
- Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi. 2020. Dataset cartography: Mapping and diagnosing datasets with training dynamics. arXiv (2020).Google Scholar
- Jinsung Yoon, Sercan Arik, and Tomas Pfister. 2020. Data valuation using reinforcement learning. In ICML.Google Scholar
Index Terms
- 2nd International Workshop on Data Quality Assessment for Machine Learning
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