Deep neural networks (DNNs) have received tremendous attention and achieved great success in various applications, such as image and video analysis, natural language processing, recommendation systems, and drug discovery. However, inherent uncertainties derived from different root causes have been serious hurdles for DNNs to find robust and trustworthy solutions for real-world problems. A lack of consideration of such uncertainties may lead to unnecessary risk. For example, a self-driving autonomous car can misclassify a human on the road. A deep learning-based medical assistant may misdiagnose cancer as a benign tumor. Uncertainty has become increasingly important, and it has been attracting attention from academia and industry due to its increased popularity in real-world applications with uncertain concerns. It also emphasizes decision-making problems, such as autonomous driving and diagnosis systems. Therefore, the wave of research at the intersection of uncertainty reasoning and quantification in data mining and machine learning has also influenced other fields of science, including computer vision, natural language processing, reinforcement learning, and social science. 

Important Dates

The following are the proposed important dates for the workshop. All deadlines are due 11:59 pm Pacific Time.

• Paper Submission: April 30th, 2026 May 15th, 2026

• Paper Notification: June 4th, 2026

• Workshop Date: TBA

Topics of Interest  

This workshop will provide a premium platform for both research and industry from different backgrounds to exchange ideas on opportunities, challenges, and cutting-edge techniques in uncertainty reasoning and quantification. We encourage submissions in various degrees of progress, such as new results, visions, techniques, innovative application papers, and progress reports under the topics that include, but are not limited to, the following broad categories:

• Uncertainty quantification in foundation models

• Uncertainty reasoning in foundation models

• Decision-making with foundation models

• Uncertainty quantification in classification and regression

• Out-of-distribution detection

• Conditional reasoning with uncertainty

• Quantification of multidimensional uncertainty

• Sequential uncertainty estimation

• Interpretation of uncertainty

• Uncertainty-aware deep reinforcement learning

• Decision-making with uncertainty

And with a particular focus, but not limited to, these application domains:

• Application of an autonomous system

• Application of uncertainty methods in a large-scale dataset

• Computer vision (uncertainty in face recognition, object relation)

• Natural language processing (language uncertainty, sentence uncertainty)

• Reinforcement learning (uncertainty-aware offline reinforcement learning exploration vs. exploitation)

• Application of uncertainty methods in foundation models

Submission Guidelines

Submissions are limited to a total of 5 pages, including all content and references. There will be no page limit for supplemental materials. All submissions must be in PDF format and use ACM Conference Proceeding templates (two-column format). One recommended setting for a Latex file of an anonymous manuscript is: \documentclass[sigconf, anonymous, review]{acmart}. Template guidelines are here: https://www.acm.org/publications/proceedings-template.

Following this KDD conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well-executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible.

Submit your paper through the UDM workshop CMT submission site: https://cmt3.research.microsoft.com/UDM2026/ 

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. 

Paper Acceptance 

Accepted workshop papers will be posted on the workshop website, but will NOT be included in the official KDD proceedings.

Upon notification, we ask that authors of accepted works deanonymize their papers, make any final changes, and then submit a camera-ready version to the CMT submission site. The workshop website will then be updated with links to accepted papers. Note that accepted works will not be formally published. This means that:

• Authors can retain full copyright of their works.

• Work contained in accepted papers is not precluded from being published in other research venues.

• Submitted papers are allowed to have significant overlap with previously published or currently submitted work (in this case, please indicate overlapping works).

Any questions may be directed to the email address: chen_zhao@baylor.edu

Attendence

For each accepted paper, at least one author must attend the conference and present the paper.

Call for reviewers: https://docs.google.com/forms/d/e/1FAIpQLSf1YHPv3i6A9ld1S4BhUEdde8umWiKQt8nOagqbUxpXuymHiw/viewform?usp=publish-editor 

TBA

• The 1st Workshop on Uncertainty Reasoning and Quantification in Decision Making, AAAI 2023, Washington D.C., USA

• The 2nd Workshop on Uncertainty Reasoning and Quantification in Decision Making, KDD 2023, Long Beach, California, USA

• The 3rd Workshop on Uncertainty Reasoning and Quantification in Decision Making, KDD 2024, Barcelona, Spain

• The 4th Workshop on Uncertainty Reasoning and Quantification in Decision Making, KDD 2025, Toronto, Ontario, Canada