122 citations · 218 across the 12 of their papers we have counts for
14 papers
Learning Rules for Materials Properties and Functions
Mario Boley, Matthias Scheffler
In materials science and engineering, one is typically searching for materials that exhibit exceptional performance for a certain function, and the number of these materials is ext…
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…
Adaptive Communication Bounds for Distributed Online Learning
Michael Kamp, Mario Boley, Michael Mock +3
We consider distributed online learning protocols that control the exchange of information between local learners in a round-based learning scenario. The learning performance of su…
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…
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
Panagiotis Mandros, Mario Boley, Jilles Vreeken
The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data. In this paper, we systematically explore the algorit…
Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups
Janis Kalofolias, Mario Boley, Jilles Vreeken
Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populatio…
Discovering Reliable Approximate Functional Dependencies
Panagiotis Mandros, Mario Boley, Jilles Vreeken
Given a database and a target attribute of interest, how can we tell whether there exists a functional, or approximately functional dependence of the target on any set of other att…
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery
Mario Boley, Bryan R. Goldsmith, Luca M. Ghiringhelli +1
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or…
Uncovering structure-property relationships of materials by subgroup discovery
B. R. Goldsmith, M. Boley, J. Vreeken +2
Subgroup discovery (SGD) is presented here as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-scie…
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…