1 citations · 1 across the 2 of their papers we have counts for
2 papers
stat.ML2019
Ancestral causal learning in high dimensions with a human genome-wide application
Umberto Noè, Bernd Taschler, Joachim Täger +2
We consider learning ancestral causal relationships in high dimensions. Our approach is driven by a supervised learning perspective, with discrete indicators of causal relationship…
stat.ML2019★ 1 cited
Model-based clustering in very high dimensions via adaptive projections
Bernd Taschler, Frank Dondelinger, Sach Mukherjee
Mixture models are a standard approach to dealing with heterogeneous data with non-i.i.d. structure. However, when the dimension is large relative to sample size and where…