Call for Abstracts

BayLearn 2026 will be an in-person event, held on Thursday, October 15th, 2026.

BayLearn 2026 will be hosted at Santa Clara University and co-organized in partnership with the University of California, Santa Cruz.

Note: BayLearn 2026 will not be a hybrid event, and it will not be live-streamed.

The BayLearn 2026 abstract submission site is now open for submissions:

Submit via Microsoft CMT: https://cmt3.research.microsoft.com/BAYLEARN2026

Please submit abstracts as a 2-page pdf in NeurIPS 2023 format. An extra page for acknowledgements and references is allowed.

About BayLearn

The BayLearn Symposium is an annual gathering of machine learning researchers and scientists from the San Francisco Bay Area. While BayLearn promotes community building and technical discussions between local researchers from academic and industrial institutions, it also welcomes visitors. This one-day event combines invited talks, contributed talks, and posters, to foster exchange of ideas.

https://baylearn.org/

Meet with fellow Bay Area machine learning researchers and scientists during the symposium that will be held on October 16th, at Santa Clara University.

Feel free to circulate this invitation to your colleagues and relevant contacts.

Key Dates

• Abstract submission deadline: Thu, Jul 30, 2026 at 11:59pm PDT
• Author notifications: Mon, Sep 14th, 2026
• Registration Lottery opens: Tue, Sep 15th, 2026
• Symposium: Thu, Oct 15th, 2026

Submissions

We encourage submission of abstracts. Acceptable material includes work which has already been submitted or published, preliminary results, and controversial findings. We do not intend to publish paper proceedings; only abstracts will be shared through an online repository. Our primary goal is to foster discussion! For examples of previously accepted talks, please watch the paper presentations from previous BayLearn Symposiums: https://baylearn.org/previous

Submit your abstracts via CMT: https://cmt3.research.microsoft.com/BAYLEARN2026

Relevant Topics

We welcome submissions on the theoretical, experimental, applied and positional arguments on Machine Learning. We invite abstracts on all topics related Machine Learning, including but not limited to the topics highlighted in NeurIPS:

• Computer vision
• Language and multimodal language models
• Robotics, embodied systems, and engineering
• AI/ML for physical sciences
• AI/ML for health and biotechnology
• AI/ML for sustainability
• AI/ML for social sciences
• AI/ML for creatives
• Neuroscience and cognitive science
• Socio-technical aspects of AI
• Human interaction in AI systems
• Decision-making, reinforcement learning, and control
• Generalization and multi-task learning
• Optimization
• Probabilistic methods
• AI and network science
• Data-centric aspects of AI
• SysML Infrastructure
• Theory
• Deep learning
• General machine learning: core contributions in supervised and unsupervised methods

Submission guidelines

All submissions must be made before the deadline to Microsoft CMT.

Please use the Latex template for NeurIPS, available in this link: NeurIPS format

While abstracts can be based on work that has been published elsewhere, the abstract should be original text of a self-contained idea and not copy-paste from a previously accepted paper.

All submissions must be anonymized and must not include any information that could intentionally or unintentionally compromise the double-blind review process. Authors may share versions of their work on preprint platforms such as arXiv. The conference will be held in-person. At least one author for each accepted abstract is requested to physically attend the conference to present their work.

The organizers will desk reject submissions based on the following criteria: * Have formatting issues, e.g., not following the formatting guidelines or template. * Exceed the 2 page limit. This includes using hyperlinks for extended content. * Have non-anonymized submissions.

High quality abstracts satisfactorily address the following:

• Motivation & Problem Framing
  • Is the problem clearly stated?
  • Is there a compelling reason this work matters (scientifically or practically)?
• Novelty / Idea Contribution
  • Does the work introduce a new idea, perspective, or research question?
• Clarity and Organization
  • Is the abstract understandable?
  • Is the abstract well-structured and relatively self-contained?
• Results / Proofs / Findings
  • Does the abstract include evidence?
  • Evidence includes but is not limited to experiments, qualitative results, case studies, examples, analysis, etc. Preliminary results are acceptable (and encouraged)
• Conclusions and Discussion
  • Does the work provide any conclusions, insights or lessons?
  • Does this work invite discussion or debate? Would this spark conversation at the event?
• Broad Applicability
  • Why should the broader ML community care about this? What is the broader impact of this work?
  • Is this exciting to a larger audience?

Note: The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.