Recent advances in machine learning (ML) systems have made it incredibly easier to train ML models given a training set. However, our understanding of the behavior of the model training process has not been improving at the same pace. Consequently, a number of key questions remain: How can we systematically assign importance or value to training data with respect to the utility of the trained models, may it be accuracy, fairness, or robustness? How does noise in the training data, either injected by noisy data acquisition processes or adversarial parties, have an impact on the trained models? How can we find the right data that can be cleaned and labeled to improve the utility of the trained models? Just when we start to understand these important questions for ML models in isolation recently, we now have to face the reality that most real-world ML applications are way more complex than a single ML model.<\/p>\n
In this article\u2014an extended abstract for an invited talk at the DEEM workshop\u2014we will discuss our current efforts in revisiting these questions for an end-to-end ML pipeline, which consists of a
\nnoise model for data and a feature extraction pipeline, followed by the training of an ML model. In our opinion, this poses a unique challenge on the joint analysis of data processing and learning. Although we will describe some of our recent results towards understanding this interesting problem, this article is more of a \u201cconfession\u201d on our technical struggles and a \u201ccry for help\u201d to our data management community.<\/p>\n","protected":false},"excerpt":{"rendered":"
Recent advances in machine learning (ML) systems have made it incredibly easier to train ML models given a training set. However, our understanding of the behavior of the model training process has not been improving at the same pace. Consequently, a number of key questions remain: How can we systematically assign importance or value to […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"DEEM@SIGMOD 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