16 citations · 59 across the 14 of their papers we have counts for
5 papers · 1 filter
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise
Wenbo Gong, Joel Jennings, Cheng Zhang +1
Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains such as climate science, finance, and healthc…
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
Wenbo Gong, Digory Smith, Zichao Wang +5
In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify th…
Deep Structural Causal Shape Models
Rajat Rasal, Daniel C. Castro, Nick Pawlowski +1
Causal reasoning provides a language to ask important interventional and counterfactual questions beyond purely statistical association. In medical imaging, for example, we may wan…
Structured Uncertainty in the Observation Space of Variational Autoencoders
James Langley, Miguel Monteiro, Charles Jones +2
Variational autoencoders (VAEs) are a popular class of deep generative models with many variants and a wide range of applications. Improvements upon the standard VAE mostly focus o…
Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster +9
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery…