TA4b.4

Non-Stationary Causal Learning via Hierarchical Modeling

Daniel Waxman, Petar Djuric, Stony Brook University, United States
Session:
TA4b: Advances in Causal Inference - Theory and Applications Lecture
Track:
Adaptive Systems, Machine Learning, and Data Analytics
Location:
Nautilus
Presentation Time:
Tue, 28 Oct, 11:30 - 11:55 PT (UTC -8)
Session Co-Chairs:
Petar Djurić, Stony Brook University and Daniel Waxman, Stony Brook University
Presentation
Discussion
Resources
No resources available.
Session TA4b
TA4b.1: Non-negative DAG Learning from Time-Series Data
Samuel Rey, King Juan Carlos University, Spain; Gonzalo Mateos, University of Rochester, United States
TA4b.2: Subgraph-Based Causal Discovery under Non-Linear Models and Soft Interventions
Chen Peng, Urbashi Mitra, University of Southern California, United States
TA4b.3: Rethinking Chronological Causal Discovery with Signal Processing
Kurt Butler, Damian Machlanski, Panagiotis Dimitrakopoulos, Sotirios Tsaftaris, University of Edinburgh, United Kingdom
TA4b.4: Non-Stationary Causal Learning via Hierarchical Modeling
Daniel Waxman, Petar Djuric, Stony Brook University, United States
Contacts