Publications

Robert Peharz; Antonio Vergari; Karl Stelzner; Alejandro Molina; Martin Trapp; Xiaoting Shao; Kristian Kersting; Zoubin Ghahramani

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

In: Amir Globerson; Ricardo Silva (Hrsg.). Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence. Conference in Uncertainty in Artificial Intelligence (UAI-2019), July 22-25, Tel Aviv, Israel, Pages 334-344, Proceedings of Machine Learning Research, Vol. 115, AUAI Press, 2019.

Claas Völcker; Alejandro Molina; Johannes Neumann; Dirk Westermann; Kristian Kersting

DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets

In: Peggy Cellier; Kurt Driessens (Hrsg.). Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-2019), International Workshops of ECML PKDD 2019, Proceedings, Part I, September 16-20, Würzburg, Germany, Pages 28-43, Communications in Computer and Information Science, Vol. 1167, Springer, 2019.

Navdeep Kaur; Gautam Kunapuli; Saket Joshi; Kristian Kersting; Sriraam Natarajan

Neural Networks for Relational Data

In: Dimitar Kazakov; Can Erten (Hrsg.). Inductive Logic Programming - 29th International Conference, Proceedings. International Conference on Inductive Logic Programming (ILP-2019), September 3-5, Plovdiv, Bulgaria, Pages 62-71, Lecture Notes in Computer Science (LNAI), Vol. 11770, Springer, 2019.

Karl Stelzner; Robert Peharz; Kristian Kersting

Faster Attend-Infer-Repeat with Tractable Probabilistic Models

In: Kamalika Chaudhuri; Ruslan Salakhutdinov (Hrsg.). Proceedings of the 36th International Conference on Machine Learning. International Conference on Machine Learning (ICML-2019), June 9-15, Long Beach, California, USA, Pages 5966-5975, Proceedings of Machine Learning Research, Vol. 97, PMLR, 2019.

Alejandro Molina; Antonio Vergari; Karl Stelzner; Robert Peharz; Pranav Subramani; Nicola Di Mauro; Pascal Poupart; Kristian Kersting

SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks

In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1901.03704, Pages 0-10, arXiv, 2019.

Michael Benedikt; Kristian Kersting; Phokion G. Kolaitis; Daniel Neider

Logic and Learning (Dagstuhl Seminar 19361)

In: Dagstuhl Reports, Vol. 9, No. 9, Pages 1-22, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2019.

Benjamin Hilprecht; Carsten Binnig; Uwe Röhm

Towards learning a partitioning advisor with deep reinforcement learning

In: Rajesh Bordawekar; Oded Shmueli (Hrsg.). Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM-2019), aiDM@SIGMOD, July 5, Amsterdam, Netherlands, Pages 6:1-6:4, ACM, 2019.

Abdallah Salama; Alexander Linke; Igor Pessoa Rocha; Carsten Binnig

XAI: A Middleware for Scalable AI

In: Slimane Hammoudi; Christoph Quix; Jorge Bernardino (Hrsg.). Proceedings of the 8th International Conference on Data Science, Technology and Applications. International Conference on Data Science, Technology and Applications (DATA-2019), July 26-28, Prague, Czech Republic, Pages 109-120, SciTePress, 2019.

Benjamin Hilprecht; Carsten Binnig; Uwe Röhm

Learning a Partitioning Advisor with Deep Reinforcement Learning

In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1904.01279, Pages 0-10, arXiv, 2019.

Nathaniel Weir; Andrew Crotty; Alex Galakatos; Amir Ilkhechi; Shekar Ramaswamy; Rohin Bhushan; Ugur Çetintemel; Prasetya Utama; Nadja Geisler; Benjamin Hättasch; Steffen Eger; Carsten Binnig

DBPal: Weak Supervision for Learning a Natural Language Interface to Databases

In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1909.06182, Pages 0-10, arXiv, 2019.