AMLDS 2025 Call for Papers


Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas but are not limited to:


Machine Learning Foundations

  • Machine Learning System Design
  • Machine Learning Optimization
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Statistical Learning
  • Transfer learning
  • Extreme Learning Machines
  • Kernel Based Learning
  • Bayesian Learning
  • Instruction Based Learning
  • Adversarial Machine Learning
    		•

    Deep Learning and Data Engineering

  • Deep Neural Networks Optimization Algorithms
  • Deep Feedforward Networks
  • Regularization
  • Deep Convolutional Neural Networks
  • Deep Recurrent Neural Networks
  • Sequence Modelling
  • Deep Generative Models
  • Generative Adversarial Networks
  • Inference Dependencies on Multi-Layered Networks
  • Tensors for Deep Learning
  • Multi Scale Deep Architecture and Learning
    		•
     

    Machine Learning and Data Engineering

  • Machine Learning in Data Lakes
  • Machine Learning based Data Integration and Data Interoperability
  • Machine Learning Data Pipelines
  • Machine Learning based Data Streaming
  • Machine Learning Relating to Knowledge and Data Management
  • Machine Learning Principles of Information Extraction from Big Data
  • Machine Learning based Web Data Management and Deep Web
  • Machine Learning Architecture for Pattern Recognition
  • Machine Learning Architecture for Medical Imaging
  • Machine Learning Search Engine
  • Machine Learning Cloud Services
  • Machine Learning IoT Services
    		•   

    Applications

  • Bioinformatics
  • Biomedical informatics
  • Computational Biology
  • Healthcare
  • Human Activity Recognition
  • Computer vision
  • Natural Language Processing
    		•

    Policies

    Submission:

    Please submit your full papers or abstracts via: https://easychair.org/conferences/?conf=amlds2025 before February 10, 2025. For detailed guideline for submission, please click.

    Changes of title/abstract/authorship:

    Authors should include a full title for their paper, as well as a complete paper by the paper submission deadline. Submission titles should not be modified after the paper submission deadline. Submissions violating these rules may be deleted after the paper submission deadline without reviewing.

    Double-Blind Review:

  • All submissions must be anonymized and may not contain any information with the intention or consequence of violating the double-blind reviewing policy, including (but not limited to) citing previous works of the authors or sharing links in a way that can infer any author’s identity or institution, actions that reveal the identities of the authors to potential reviewers.
  • Authors are allowed to post versions of their work on preprint servers such as arXiv. They are also allowed to give talks to restricted audiences on the work(s) submitted to AMLDS during the review. If you have posted or plan to post a non-anonymized version of your paper online before the ICML decisions are made, the submitted version must not refer to the non-anonymized version.

    Dual Submission:

    It is not appropriate to submit papers that are identical (or substantially similar) to versions that have been previously published, accepted for publication, or submitted in parallel to other conferences or journals. Such submissions violate our dual submission policy, and the organizers have the right to reject such submissions, or to remove them from the proceedings. Note that submissions that have been or are being presented at workshops do not violate the dual-submission policy, as long as there’s no associated archival publication.

    Reviewing Criteria:

    Accepted papers must be based on original research and must contain novel results of significant interest to the machine learning community. Results can be either theoretical or empirical. Results will be judged on the degree to which they have been objectively established and/or their potential for scientific and technological impact. Reproducibility of results and easy availability of code will be taken into account in the decision-making process whenever appropriate.

    Ethics:

    Authors and members of the program committee, including reviewers, are expected to follow standard ethical guidelines. Plagiarism in any form is strictly forbidden .