activity
20172021
most citedDiscovering Reliable Approximate Functional Dependencies

49 citations · 51 across the 6 of their papers we have counts for

collaborators

10 papers

cond-mat.mtrl-sci20212 cited

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…

cs.LG2021

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…

cs.LG2020

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…

cs.LG2019

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…

cs.DC2019

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…

cs.LG2019

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…