67 citations · 118 across the 22 of their papers we have counts for
23 papers
Computing Expected Motif Counts for Exchangeable Graph Generative Models
Oliver Schulte
Estimating the expected value of a graph statistic is an important inference task for using and learning graph models. This note presents a scalable estimation procedure for expect…
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte +1
Conventional supervised learning methods typically assume i.i.d samples and are found to be sensitive to out-of-distribution (OOD) data. We propose Generative Causal Representation…
From Graph Generation to Graph Classification
Oliver Schulte
This note describes a new approach to classifying graphs that leverages graph generative models (GGM). Assuming a GGM that defines a joint probability distribution over graphs and…
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
Xiangyu Sun, Oliver Schulte
A fundamental problem of causal discovery is cause-effect inference, learning the correct causal direction between two random variables. Significant progress has been made through…
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf +1
Generative models for graph data are an important research topic in machine learning. Graph data comprise two levels that are typically analyzed separately: node-level properties s…
Pre and Post Counting for Scalable Statistical-Relational Model Discovery
Richard Mar, Oliver Schulte
Statistical-Relational Model Discovery aims to find statistically relevant patterns in relational data. For example, a relational dependency pattern may stipulate that a user's gen…