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Abstract: What can be learned from data? This fundamental question in machine learning takes on new complexity in modern pipelines where classical assumptions fail—both in how data is generated and in how learning objectives are defined. This thesis develops foundations for learning under these complex conditions, revealing how violations of traditional assumptions transform not just…