11 citations · 23 across the 6 of their papers we have counts for
6 papers
Variational Boosted Soft Trees
Tristan Cinquin, Tammo Rukat, Philipp Schmidt +2
Gradient boosting machines (GBMs) based on decision trees consistently demonstrate state-of-the-art results on regression and classification tasks with tabular data, often outperfo…
Bayesian Nonparametric Boolean Factor Models
Tammo Rukat, Christopher Yau
We build upon probabilistic models for Boolean Matrix and Boolean Tensor factorisation that have recently been shown to solve these problems with unprecedented accuracy and to enab…
TensOrMachine: Probabilistic Boolean Tensor Decomposition
Tammo Rukat, Chris C. Holmes, Christopher Yau
Boolean tensor decomposition approximates data of multi-way binary relationships as product of interpretable low-rank binary factors, following the rules of Boolean algebra. Here,…
An interpretable latent variable model for attribute applicability in the Amazon catalogue
Tammo Rukat, Dustin Lange, Cédric Archambeau
Learning attribute applicability of products in the Amazon catalog (e.g., predicting that a shoe should have a value for size, but not for battery-type at scale is a challenge. The…
Bayesian Boolean Matrix Factorisation
Tammo Rukat, Chris C. Holmes, Michalis K. Titsias +1
Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the ot…
Resting state brain networks from EEG: Hidden Markov states vs. classical microstates
Tammo Rukat, Adam Baker, Andrew Quinn +1
Functional brain networks exhibit dynamics on the sub-second temporal scale and are often assumed to embody the physiological substrate of cognitive processes. Here we analyse the…