32 citations · 39 across the 4 of their papers we have counts for
5 papers
CryptoSPN: Privacy-preserving Sum-Product Network Inference
Amos Treiber, Alejandro Molina, Christian Weinert +2
AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e.g.,…
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
Alejandro Molina, Antonio Vergari, Karl Stelzner +5
We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called…
Sum-Product Networks for Hybrid Domains
Alejandro Molina, Antonio Vergari, Nicola Di Mauro +3
While all kinds of mixed data -from personal data, over panel and scientific data, to public and commercial data- are collected and stored, building probabilistic graphical models…
Coresets for Dependency Networks
Alejandro Molina, Alexander Munteanu, Kristian Kersting
Many applications infer the structure of a probabilistic graphical model from data to elucidate the relationships between variables. But how can we train graphical models on a mass…
Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage
Elena Erdmann, Karin Boczek, Lars Koppers +7
Migration crisis, climate change or tax havens: Global challenges need global solutions. But agreeing on a joint approach is difficult without a common ground for discussion. Publi…