Kevin Innerebner

According to our database, Kevin Innerebner authored at least 12 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling.

Kevin Innerebner, Franz M. Rohrhofer, Bernhard C. Geiger

CoRR, July, 2025

Hybrid AI for Human-Centric Personalization (HyPer).

Elisabeth Lex, Kevin Innerebner, Marko Tkalcic, Dominik Kowald, Markus Schedl

Proceedings of the Adjunct Proceedings of the 33rd ACM Conference on User Modeling, 2025

Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R.

Kevin Innerebner, Dominik Kowald, Markus Schedl, Elisabeth Lex

Proceedings of the Adjunct Proceedings of the 33rd ACM Conference on User Modeling, 2025

Differentiable Fuzzy Neural Networks for Recommender Systems.

Stephan Bartl, Kevin Innerebner, Elisabeth Lex

Proceedings of the Adjunct Proceedings of the 33rd ACM Conference on User Modeling, 2025

OnSET: Ontology and Semantic Exploration Toolkit.

Benedikt Kantz, Kevin Innerebner, Peter Waldert, Stefan Lengauer, Elisabeth Lex, Tobias Schreck

Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025

2024

FemQuest - An Interactive Multiplayer Game to Engage Girls in Programming.

Michael Holly, Lisa Habich, Maria Seiser, Florian Glawogger, Kevin Innerebner, Sandra Kupsa, Philipp Einwallner, Johanna Pirker

Proceedings of the IEEE Conference on Games, 2024

2023

DAPHNE Runtime: Harnessing Parallelism for Integrated Data Analysis Pipelines.

Aristotelis Vontzalidis, Stratos Psomadakis, Constantinos Bitsakos, Mark Dokter, Kevin Innerebner, Patrick Damme, Matthias Boehm, Florina M. Ciorba, Ahmed Eleliemy, Vasileios Karakostas, Ales Zamuda, Dimitrios Tsoumakos

Proceedings of the Euro-Par 2023: Parallel Processing Workshops - Euro-Par 2023 International Workshops, Limassol, Cyprus, August 28, 2023

2022

Federated Data Preparation, Learning, and Debugging in Apache SystemDS.

Sebastian Baunsgaard, Matthias Boehm, Kevin Innerebner, Mito Kehayov, Florian Lackner, Olga Ovcharenko, Arnab Phani, Tobias Rieger, David Weissteiner, Sebastian Benjamin Wrede

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines.

Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina M. Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios I. Goumas, Dirk Habich, Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaz Kosar, Alexander Krause, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies, Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pinar Tözün, Wojciech Ulatowski, Yuanyuan Wang, Izajasz P. Wrosz, Ales Zamuda, Ce Zhang, Xiaoxiang Zhu

Proceedings of the 12th Conference on Innovative Data Systems Research, 2022

2021

ExDRa: Exploratory Data Science on Federated Raw Data.

Sebastian Baunsgaard, Matthias Boehm, Ankit Chaudhary, Behrouz Derakhshan, Stefan Geißelsöder, Philipp M. Grulich, Michael Hildebrand, Kevin Innerebner, Volker Markl, Claus Neubauer, Sarah Osterburg, Olga Ovcharenko, Sergey Redyuk, Tobias Rieger, Alireza Rezaei Mahdiraji, Sebastian Benjamin Wrede, Steffen Zeuch

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

2020

SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle.

Matthias Boehm, Iulian Antonov, Sebastian Baunsgaard, Mark Dokter, Robert Ginthör, Kevin Innerebner, Florijan Klezin, Stefanie N. Lindstaedt, Arnab Phani, Benjamin Rath, Berthold Reinwald, Shafaq Siddiqui, Sebastian Benjamin Wrede

Proceedings of the 10th Conference on Innovative Data Systems Research, 2020

2019

SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle.

Matthias Boehm, Iulian Antonov, Mark Dokter, Robert Ginthör, Kevin Innerebner, Florijan Klezin, Stefanie N. Lindstaedt, Arnab Phani, Benjamin Rath

CoRR, 2019