130 citations · 671 across the 42 of their papers we have counts for
4 papers · 1 filter
Cyclic Causal Discovery from Continuous Equilibrium Data
Joris Mooij, Tom Heskes
We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data. Novel aspects of the proposed method are its ability…
Learning Sparse Causal Models is not NP-hard
Tom Claassen, Joris Mooij, Tom Heskes
This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obta…
Semi-supervised Ranking Pursuit
Evgeni Tsivtsivadze, Tom Heskes
We propose a novel sparse preference learning/ranking algorithm. Our algorithm approximates the true utility function by a weighted sum of basis functions using the squared loss on…
Sparse Approximate Inference for Spatio-Temporal Point Process Models
Botond Cseke, Andrew Zammit Mangion, Tom Heskes +1
Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computa- tionally…