3rd Workshop on Uncertainty Reasoning and Quantification in Decision Making
(held in conjunction with ACM SIGKDD'24)
August 26, 2024, Barcelona, Spain

Introduction

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.

Workshop Chairs

• Xujiang Zhao (NEC Laboratories America, zhaoxuj32@gmail.com, main contact)
• Chen Zhao (Baylor University, chen_zhao@baylor.edu, main contact)
• Feng Chen (The University of Texas at Dallas)
• Jin-Hee Cho (Virginia Tech)
• Hua Wei (Arizona State University)
• Haifeng Chen (NEC Laboratories America)

Our Past Workshops

• 1st Workshop on Uncertainty Reasoning and Quantification in Decision Making, AAAI 2023, Washington D.C., USA
• 2nd Workshop on Uncertainty Reasoning and Quantification in Decision Making, KDD 2023, Long Beach, California, USA

Workshop Gallery