Workshop Agenda
Abstract
Topic: Probabilistic Deep Generative Models
Probabilistic models deal with uncertainty in data-driven decision-making and modeling in a principled manner. However, as models and datasets grow in complexity, efficient and exact inference (querying the model) becomes a challenge, hindering their feasibility in high-stakes domains like healthcare. This tutorial aims to introduce participants to Deep and Tractable Probabilistic Generative Models, a special class of generative models that balance expressiveness and tractability. Participants will learn about their theoretical foundations, practical implementations, and real-world applications.
Speaker Bio:
Dr. Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas, a hessian.AI fellow at TU Darmstadt and a RBDSCAII Distinguished Faculty Fellow at IIT Madras. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He is a AAAI senior member and has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award, ECSS Graduate teaching award from UTD and the IU trustees Teaching Award from Indiana University. He is the program chair of AAAI 2024, the general chair of CoDS-COMAD 2024, AI and society track chair of AAAI 2023 and 2022, senior member track chair of AAAI 2023, demo chair of IJCAI 2022, program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He was the specialty chief editor of Frontiers in ML and AI journal, and is an associate editor of JAIR, DAMI and Big Data journals.
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