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Data-centric Machine Learning Research
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.
Editors in Chief
- Newsha Ardalani (Meta)
- Isabelle Guyon (Google)
- Neil Lawrence (University of Cambridge)
- Joaquin Vanschoren (TU Eindhoven)
- Ce Zhang (ETH Zurich)