122 citations · 218 across the 12 of their papers we have counts for
7 papers · 1 filter
Better Short than Greedy: Interpretable Models through Optimal Rule Boosting
Mario Boley, Simon Teshuva, Pierre Le Bodic +1
Rule ensembles are designed to provide a useful trade-off between predictive accuracy and model interpretability. However, the myopic and random search components of current rule e…
Discovering Reliable Causal Rules
Kailash Budhathoki, Mario Boley, Jilles Vreeken
We study the problem of deriving policies, or rules, that when enacted on a complex system, cause a desired outcome. Absent the ability to perform controlled experiments, such rule…
Relative Flatness and Generalization
Henning Petzka, Michael Kamp, Linara Adilova +2
Flatness of the loss curve is conjectured to be connected to the generalization ability of machine learning models, in particular neural networks. While it has been empirically obs…
Communication-Efficient Distributed Online Learning with Kernels
Michael Kamp, Sebastian Bothe, Mario Boley +1
We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that repre…
Discovering Reliable Correlations in Categorical Data
Panagiotis Mandros, Mario Boley, Jilles Vreeken
In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably me…
Effective Parallelisation for Machine Learning
Michael Kamp, Mario Boley, Olana Missura +1
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predi…
Probabilistic Structured Predictors
Shankar Vembu, Thomas Gartner, Mario Boley
We consider MAP estimators for structured prediction with exponential family models. In particular, we concentrate on the case that efficient algorithms for uniform sampling from t…