85 citations · 253 across the 29 of their papers we have counts for
30 papers
Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learning
Wolfgang Maass, Sabine Janzen, Prajvi Saxena +1
We introduce Afferent Learning, a framework that produces Computational Afferent Traces (CATs) as adaptive, internal risk signals for damage-avoidance learning. Inspired by biologi…
Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?
Felix Schur, Niklas Pfister, Peng Ding +2
We study the problem of estimating causal effects under hidden confounding in the following unpaired data setting: we observe some covariates and an outcome under different…
Deep Learning of Causal Structures in High Dimensions
Kai Lagemann, Christian Lagemann, Bernd Taschler +1
Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particula…
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
Konstantin Göbler, Anne Miloschewski, Mathias Drton +1
Graphical models are an important tool in exploring relationships between variables in complex, multivariate data. Methods for learning such graphical models are well developed in…
Scalable Regularised Joint Mixture Models
Thomas Lartigue, Sach Mukherjee
In many applications, data can be heterogeneous in the sense of spanning latent groups with different underlying distributions. When predictive models are applied to such data the…
On unsupervised projections and second order signals
Thomas Lartigue, Sach Mukherjee
Linear projections are widely used in the analysis of high-dimensional data. In unsupervised settings where the data harbour latent classes/clusters, the question of whether class…