6 citations · 10 across the 4 of their papers we have counts for
4 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…
Fast Learning of Relational Dependency Networks
Oliver Schulte, Zhensong Qian, Arthur E. Kirkpatrick +2
A Relational Dependency Network (RDN) is a directed graphical model widely used for multi-relational data. These networks allow cyclic dependencies, necessary to represent relation…
Computing Multi-Relational Sufficient Statistics for Large Databases
Zhensong Qian, Oliver Schulte, Yan Sun
Databases contain information about which relationships do and do not hold among entities. To make this information accessible for statistical analysis requires computing sufficien…