The Journal of Machine Learning Research (JMLR)

The Journal of Machine Learning Research (JMLR) is a premier open-access journal dedicated to the publication of high-impact research in machine learning and related fields. Founded in 2000 by leading researchers seeking an unrestricted publication platform, JMLR provides a rigorous yet timely review process, with electronic publication of finalized papers immediately upon acceptance. The journal’s scope spans theoretical advances, algorithmic design, experimental studies, and insightful applications of learning methods across disciplines. Articles often include novel models, improved optimization techniques, statistical analyses, and principled evaluations of learning systems. Unlike many traditional journals, JMLR operates a community-driven model, originally issuing paper volumes (e.g., 8 issues per year) and now disseminating research continuously online to maintain rapid access for the global community. Because the journal is open access with no publication fees for authors, it promotes broad dissemination and reuse of scientific knowledge. JMLR has been instrumental in shaping the machine learning research landscape, publishing influential works on foundational learning theory, reinforcement learning, kernel methods, deep learning, and data-centric AI. Its reputation and reach make it a respected venue for researchers and practitioners looking to contribute significant advancements to the science and engineering of learning from data.

The Journal of Machine Learning Research (JMLR) is :-

• International, Peer-Reviewed, Open Access, Refereed, Artificial Intelligence, Software, Statistics and Probability, Control and Systems Engineering, Foundations of learning theory, Algorithms and optimization methods, Statistical machine learning, Deep learning and neural networks , Online or Print , Bi-Monthly Journal

• UGC Approved, ISSN Approved: P-ISSN P-ISSN: 1532-4435, E-ISSN: 1533-7928, Established: 2001,
• Does Not Provide Crossref DOI

• Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE