This workshop seeks contributions from researchers across machine learning, statistics, philosophy of science, decision theory, and related disciplines to explore theoretical foundations, algorithmic innovations, and practical applications that center around epistemic uncertainty (EU). We welcome works-in-progress and mature research that address the central challenge of reasoning and decision-making under epistemic uncertainty.

Topics of interest include, but are not limited to

Representing and Measuring Epistemic Uncertainty

• Mathematical frameworks for EU: Bayesian methods, imprecise probability, fuzzy logic, belief functions, possibility theory, etc.

• Comparisons and formal properties of uncertainty representations

• Evaluation criteria and benchmarking strategies for uncertainty quantification methods

• Epistemic vs aleatoric uncertainty: delineation and interaction
  
  

Prediction Under Epistemic Uncertainty

• Predictive models that capture and express EU: Bayesian models, evidential deep learning, credal models

• Generalisation under distribution shifts, domain adaptation, and robustness analysis

• OOD detection and safe prediction under model misspecification

• Learning under partial or vague supervision
  
  

Decision-Making and Learning Under Epistemic Uncertainty

• Risk-sensitive and ambiguity-aware decision-making frameworks

• Uncertainty quantification in generative models

• Active learning, Bayesian experimental design, and uncertainty-aware optimisation

• EU in reinforcement learning, continual learning, and online learning setting

• Integration of principled uncertainty models into large-scale architectures (e.g., transformers, diffusion models)

• Scalable algorithms for traditionally intractable uncertainty models
  
  

We encourage both theoretical contributions and applied case studies. Submissions that challenge prevailing assumptions, propose novel benchmarks, or provide insights into the philosophical and foundational dimensions of uncertainty in AI are especially welcome.

Submissions should present novel, unpublished work. Work that previously appeared in non-archival venues (such as arXiv or other workshops without proceedings) is allowed. All submitting authors are required to have an OpenReview profile: please ensure that these are up-to-date before submitting.

Submission Instructions

To submit your paper, please consider the following instructions and guidelines:

• All contributions should be made via OpenReview. 

  • We welcome submissions of original, unpublished material, as well as work that is currently under review (i.e. has been submitted but not yet accepted elsewhere). Note that new OpenReview profiles created without an institutional email will go through a moderation process that can take up to two weeks while those created with an institutional email will be activated automatically.

• Page limit: Papers should be up to 4-6 pages, excluding references and supplementary materials.

• Template: Please use the NeurIPS 2025 Latex style files. 

• Double blind reviews: authors should anonymize their submissions to ensure a double blind review process.

• LLMs policy: In the preparation of your contributions, the use of LLMs is allowed only as a general-purpose writing assist tool.

Publication. EIML workshop is non-archival, and should thus generally not violate dual submission policies at other archival venues (e.g., submitting work that is currently under review at another conference is permitted); if unsure, please check with the corresponding venue. 

Attending the workshop. Our workshop is primarily an in-person event, and authors are asked to present a poster at the workshop if possible. A subset of papers will be selected for presentation in short 10-minute spotlight talks.