UAI 2016 - Workshops
Causation: Foundation to ApplicationRoom: American I&II, Time: 9:00am - 5:30pm (Workshop website)
Causality is central to how we view and react to the world around us, to our decision making, and to the advancement of science. For many areas of science, the ability to provide a causal account of a system is seen as the ultimate goal of understanding the system. UAI as a conference offers a venue for the latest methodological development in causal inference. In this workshop we aim to complement this strength with a focus on the foundations of causal inference on the one hand, and practical applications on the other. We envision this integration of the boundaries of the field as providing a basis for fruitful discussion among researchers who do not usually have offices on the same corridor. While foundations and applications are the focus, we welcome contributions from all areas relating to the study of causality. This one-day workshop will explore these topics through open problems session, presentations and a poster session.
Bayesian Applications WorkshopRoom: Freedom I&II, Time: 9:00am - 5:30pm (Workshop website)
Continuing a successful tradition as part of the UAI conference, the 13th Annual Bayesian Modeling Applications Workshop will provide a forum for exchange about real-world problems among applications practitioners, tool developers, and researchers. The aim of the workshop is to foster discussion on the challenges of building applications whilst considering stakeholders, user interaction, tools, knowledge elicitation, learning, validation, system integration, and deployment.
Machine Learning for HealthRoom: Enterprise I&II, Time: 8:50am - 6:20pm (Workshop website)
Machine learning is revolutionizing our understanding of many human health problems from obesity to cancer. With ever increasing amount of data coming from this domain, computational biology and medicine are also transforming the machine learning community by not only providing new applications but also inspiring new modeling frameworks and learning paradigms. The goal this workshop is to bring together machine learning scientists and computational biologists. We would like to showcase recent advances in this field and discuss challenges in computational methodology and biomedical application.