1 citations · 1 across the 4 of their papers we have counts for
5 papers
Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data
Stephen Asiedu, David Watson
Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects. Although this ordering provides a natural scaffo…
Minimax Rates and Spectral Distillation for Tree Ensembles
Binh Duc Vu, David S. Watson
Tree ensembles such as random forests (RFs) and gradient boosting machines (GBMs) are among the most widely used supervised learners, yet their theoretical properties remain incomp…
Probably Approximately Correct Maximum A Posteriori Inference
Matthew Shorvon, Frederik Mallmann-Trenn, David S. Watson
Computing the conditional mode of a distribution, better known as the maximum a posteriori (MAP) assignment, is a fundamental task in probabilistic inference. However, MAP is gener…
Multi-omic Causal Discovery using Genotypes and Gene Expression
Stephen Asiedu, David Watson
Causal discovery in multi-omic datasets is crucial for understanding the bigger picture of gene regulatory mechanisms, but remains challenging due to high dimensionality, different…
Autoencoding Random Forests
Binh Duc Vu, Jan Kapar, Marvin Wright +1
We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a…