The Journal of Data-centric Machine Learning Research (DMLR) is a new member of the JMLR family, aiming to provide a top archival venue for high-quality scholarly articles focused on the data aspect of machine learning research. Broadly defined, DMLR covers but not limited to the following topics:
  • Datasets for machine learning research.
  • Benchmarks for machine learning research (collections of datasets with particular aims).
  • Benchmarking tools and methods.
  • Methodology and empirical evaluation of data collection processes, data generation, data labeling, data augmentation processes, generalizability of datasets, feature representations, text generation models, and image generation models.
  • Societal and ethical studies around creation and uses of data.
  • Fundamental contributions (theoretical or empirical) on various aspects of data quality, including data bias, variance, uncertainty and their influence on ML.
  • Algorithms for data cleaning, acquisition, quality evaluation, and alignment for ML.
  • Prompt design and creation for generative and foundational models.
  • Experimental design, registered experiments, methodology of empirical evaluations, including design of competitions and benchmarks.
  • Frameworks for responsible dataset development, audits of existing datasets, identifying significant problems with existing datasets and their use.
  • Systematic analyses of existing systems on novel datasets or benchmarks that yield important new insight.
DMLR aims to maintain the same high-quality bar of JMLR with a rigorous review process and a high-profile, dedicated editorial board and review board with diverse expertise and representation from all adjacent fields. We will continue to refine and tailor our process to better serve the special nature of data-centric ML research. We also hope that DMLR to become the venue for innovations that are tailored to the special nature of data-centric ML research.

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