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ORCIDKristian Kersting
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- affiliation: TU Darmstadt, Computer Science Department, Germany
- affiliation: TU Darmstadt, Centre for Cognitive Science, Germany
- affiliation: TU Dortmund, Department of Computer Science, Germany
- affiliation: University of Bonn, Faculty of Agriculture, Germany
- affiliation: Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany
- affiliation: Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Cambridge, MA, USA
- affiliation (PhD 2005): University of Freiburg, Machine Learning Laborator, Germany
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2020 – today
- 2021
[c169]Xiaoting Shao, Arseny Skryagin, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting:
Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions. AAAI 2021: 9533-9540- [e12]Frank Hutter
, Kristian Kersting, Jefrey Lijffijt, Isabel Valera:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part I. Lecture Notes in Computer Science 12457, Springer 2021, ISBN 978-3-030-67657-5 [contents] - [e11]Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part II. Lecture Notes in Computer Science 12458, Springer 2021, ISBN 978-3-030-67660-5 [contents] - [e10]Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part III. Lecture Notes in Computer Science 12459, Springer 2021, ISBN 978-3-030-67663-6 [contents] - [i62]Quentin Delfosse, Patrick Schramowski, Alejandro Molina, Kristian Kersting:
Recurrent Rational Networks. CoRR abs/2102.09407 (2021) - [i61]Matej Zecevic, Devendra Singh Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting:
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models. CoRR abs/2102.10440 (2021) - [i60]Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A. Rothkopf, Kristian Kersting:
Language Models have a Moral Dimension. CoRR abs/2103.11790 (2021) - [i59]Karl Stelzner, Kristian Kersting, Adam R. Kosiorek:
Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation. CoRR abs/2104.01148 (2021) - [i58]Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, Max Mühlhäuser:
User Label Leakage from Gradients in Federated Learning. CoRR abs/2105.09369 (2021) - [i57]Matej Zecevic, Devendra Singh Dhami, Kristian Kersting:
Intriguing Parameters of Structural Causal Models. CoRR abs/2105.12697 (2021) - [i56]Nils Thoma, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting:
RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting. CoRR abs/2106.04148 (2021) - 2020
- [j53]Xiaoting Shao, Tjitze Rienstra, Matthias Thimm, Kristian Kersting:
Towards Understanding and Arguing with Classifiers: Recent Progress. Datenbank-Spektrum 20(2): 171-180 (2020) - [j52]Johannes Czech, Moritz Willig, Alena Beyer, Kristian Kersting, Johannes Fürnkranz:
Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data. Frontiers Artif. Intell. 3: 24 (2020) - [j51]Patrick Schramowski, Cigdem Turan, Sophie Jentzsch, Constantin A. Rothkopf, Kristian Kersting:
The Moral Choice Machine. Frontiers Artif. Intell. 3: 36 (2020) - [j50]Rudolf Lioutikov, Guilherme Maeda, Filipe Veiga, Kristian Kersting, Jan Peters:
Learning attribute grammars for movement primitive sequencing. Int. J. Robotics Res. 39(1) (2020) - [j49]Rafet Sifa, Raheel Yawar, Rajkumar Ramamurthy, Christian Bauckhage, Kristian Kersting:
Matrix- and Tensor Factorization for Game Content Recommendation. Künstliche Intell. 34(1): 57-67 (2020) - [j48]Kristian Kersting:
Rethinking Computer Science Through AI. Künstliche Intell. 34(4): 435-437 (2020) - [j47]Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, Carsten Binnig:
DeepDB: Learn from Data, not from Queries! Proc. VLDB Endow. 13(7): 992-1005 (2020) - [c168]Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, Kristian Kersting:
CryptoSPN: Privacy-Preserving Sum-Product Network Inference. ECAI 2020: 1946-1953 - [c167]Ute Schmid, Volker Tresp, Matthias Bethge, Kristian Kersting, Rainer Stiefelhagen:
Künstliche Intelligenz - Die dritte Welle. GI-Jahrestagung 2020: 91-95 - [c166]Alejandro Molina, Patrick Schramowski, Kristian Kersting:
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks. ICLR 2020 - [c165]Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting:
Structured Object-Aware Physics Prediction for Video Modeling and Planning. ICLR 2020 - [c164]Cigdem Turan, Patrick Schramowski, Constantin A. Rothkopf, Kristian Kersting:
Alfie: An Interactive Robot with Moral Compass. ICMI 2020: 758-759 - [c163]Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani:
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. ICML 2020: 7563-7574 - [c162]Matej Petkovic, Michelangelo Ceci, Kristian Kersting, Saso Dzeroski:
Estimating the Importance of Relational Features by Using Gradient Boosting. ISMIS 2020: 362-371 - [c161]Tjitze Rienstra, Matthias Thimm, Kristian Kersting, Xiaoting Shao:
Independence and D-separation in Abstract Argumentation. KR 2020: 713-722 - [c160]Kristian Kersting:
On Hybrid and Systems AI. LWDA 2020: 3 - [c159]Xiaoting Shao, Zhongjie Yu, Arseny Skryagin, Tjitze Rienstra, Matthias Thimm, Kristian Kersting:
Modelling Multivariate Ranking Functions with Min-Sum Networks. SUM 2020: 281-288 - [i55]Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, Kristian Kersting:
Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations. CoRR abs/2001.05371 (2020) - [i54]Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, Kristian Kersting:
CryptoSPN: Privacy-preserving Sum-Product Network Inference. CoRR abs/2002.00801 (2020) - [i53]Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani:
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. CoRR abs/2004.06231 (2020) - [i52]Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting:
Fitted Q-Learning for Relational Domains. CoRR abs/2006.05595 (2020) - [i51]Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, Marion Neumann:
TUDataset: A collection of benchmark datasets for learning with graphs. CoRR abs/2007.08663 (2020) - [i50]Cigdem Turan, Patrick Schramowski, Constantin A. Rothkopf, Kristian Kersting:
Alfie: An Interactive Robot with a Moral Compass. CoRR abs/2009.05349 (2020) - [i49]Wolfgang Stammer, Patrick Schramowski, Kristian Kersting:
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations. CoRR abs/2011.12854 (2020) - [i48]Sophie Burkhardt, Jannis Brugger, Nicolas Wagner, Zahra Ahmadi, Kristian Kersting, Stefan Kramer:
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation. CoRR abs/2012.08459 (2020) - [i47]Johannes Czech, Patrick Korus, Kristian Kersting:
Monte-Carlo Graph Search for AlphaZero. CoRR abs/2012.11045 (2020) - [i46]Kristian Kersting, Miryung Kim, Guy Van den Broeck, Thomas Zimmermann:
SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091). Dagstuhl Reports 10(2): 76-87 (2020)
2010 – 2019
- 2019
- [j46]Laura Antanas, Plinio Moreno, Marion Neumann, Rui Pimentel de Figueiredo, Kristian Kersting, José Santos-Victor, Luc De Raedt:
Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach. Auton. Robots 43(6): 1393-1418 (2019) - [j45]Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel:
A unifying view of explicit and implicit feature maps of graph kernels. Data Min. Knowl. Discov. 33(6): 1505-1547 (2019) - [j44]Andrea Galassi, Kristian Kersting, Marco Lippi, Xiaoting Shao, Paolo Torroni:
Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning. Frontiers Big Data 2: 52 (2019) - [j43]Fabrizio Riguzzi, Kristian Kersting, Marco Lippi, Sriraam Natarajan:
Editorial: Statistical Relational Artificial Intelligence. Frontiers Robotics AI 6: 68 (2019) - [j42]Anna Brugger, Jan Behmann, Stefan Paulus, Hans-Georg Luigs, Matheus Thomas Kuska, Patrick Schramowski, Kristian Kersting, Ulrike Steiner, Anne-Katrin Mahlein:
Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range. Remote. Sens. 11(12): 1401 (2019) - [c158]Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera:
Automatic Bayesian Density Analysis. AAAI 2019: 5207-5215 - [c157]Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli, Kristian Kersting, Sriraam Natarajan:
Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs. AAAI 2019: 7816-7824 - [c156]Sophie Jentzsch, Patrick Schramowski, Constantin A. Rothkopf, Kristian Kersting:
Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices. AIES 2019: 37-44 - [c155]Stefano Teso, Kristian Kersting:
Explanatory Interactive Machine Learning. AIES 2019: 239-245 - [c154]Lukas Weber, Lukas Sommer, Julian Oppermann, Alejandro Molina, Kristian Kersting, Andreas Koch:
Resource-Efficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs. FPT 2019: 251-254 - [c153]Karl Stelzner, Robert Peharz, Kristian Kersting:
Faster Attend-Infer-Repeat with Tractable Probabilistic Models. ICML 2019: 5966-5975 - [c152]Navdeep Kaur, Gautam Kunapuli, Saket Joshi, Kristian Kersting, Sriraam Natarajan:
Neural Networks for Relational Data. ILP 2019: 62-71 - [c151]Stefan Lüdtke, Alejandro Molina, Kristian Kersting, Thomas Kirste:
Gaussian Lifted Marginal Filtering. KI 2019: 230-243 - [c150]Claas Völcker, Alejandro Molina, Johannes Neumann, Dirk Westermann, Kristian Kersting:
DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets. PKDD/ECML Workshops (1) 2019: 28-43 - [c149]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. UAI 2019: 334-344 - [i45]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. CoRR abs/1901.03704 (2019) - [i44]Kristian Kersting, Jan Peters, Constantin A. Rothkopf:
Was ist eine Professur fuer Kuenstliche Intelligenz? CoRR abs/1903.09516 (2019) - [i43]Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting:
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. CoRR abs/1905.08550 (2019) - [i42]Andrea Galassi, Kristian Kersting, Marco Lippi, Xiaoting Shao, Paolo Torroni:
Neural-Symbolic Argumentation Mining: an Argument in Favour of Deep Learning and Reasoning. CoRR abs/1905.09103 (2019) - [i41]Parisa Kordjamshidi, Dan Roth, Kristian Kersting:
Declarative Learning-Based Programming as an Interface to AI Systems. CoRR abs/1906.07809 (2019) - [i40]Alejandro Molina, Patrick Schramowski, Kristian Kersting:
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks. CoRR abs/1907.06732 (2019) - [i39]Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting:
Random Sum-Product Forests with Residual Links. CoRR abs/1908.03250 (2019) - [i38]Johannes Czech, Moritz Willig, Alena Beyer, Kristian Kersting, Johannes Fürnkranz:
Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data. CoRR abs/1908.06660 (2019) - [i37]Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, Carsten Binnig:
DeepDB: Learn from Data, not from Queries! CoRR abs/1909.00607 (2019) - [i36]Navdeep Kaur, Gautam Kunapuli, Saket Joshi, Kristian Kersting, Sriraam Natarajan:
Neural Networks for Relational Data. CoRR abs/1909.04723 (2019) - [i35]Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting:
Structured Object-Aware Physics Prediction for Video Modeling and Planning. CoRR abs/1910.02425 (2019) - [i34]Patrick Schramowski, Cigdem Turan, Sophie Jentzsch, Constantin A. Rothkopf, Kristian Kersting:
BERT has a Moral Compass: Improvements of ethical and moral values of machines. CoRR abs/1912.05238 (2019) - [i33]Michael Benedikt, Kristian Kersting, Phokion G. Kolaitis, Daniel Neider:
Logic and Learning (Dagstuhl Seminar 19361). Dagstuhl Reports 9(9): 1-22 (2019) - 2018
- [j41]Kristian Kersting:
Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines. Frontiers Big Data 1: 6 (2018) - [j40]Kristian Kersting, Ulrich Meyer:
From Big Data to Big Artificial Intelligence? - Algorithmic Challenges and Opportunities of Big Data. Künstliche Intell. 32(1): 3-8 (2018) - [j39]Kristian Kersting:
Making AI Smarter. Künstliche Intell. 32(4): 227-229 (2018) - [c148]Alejandro Molina, Alexander Munteanu, Kristian Kersting:
Core Dependency Networks. AAAI 2018: 3820-3827 - [c147]Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting:
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. AAAI 2018: 3828-3835 - [c146]Antonio Vergari, Robert Peharz, Nicola Di Mauro, Alejandro Molina, Kristian Kersting, Floriana Esposito:
Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks. AAAI 2018: 4163-4170 - [c145]Lukas Sommer, Julian Oppermann, Alejandro Molina, Carsten Binnig, Kristian Kersting, Andreas Koch:
Automatic Mapping of the Sum-Product Network Inference Problem to FPGA-Based Accelerators. ICCD 2018: 350-357 - [c144]Rudolf Lioutikov, Guilherme Maeda, Filipe Veiga, Kristian Kersting, Jan Peters:
Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives. ICRA 2018: 1-8 - [c143]Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle, Kristian Kersting:
Efficient Symbolic Integration for Probabilistic Inference. IJCAI 2018: 5031-5037 - [c142]Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste:
Lifted Filtering via Exchangeable Decomposition. IJCAI 2018: 5067-5073 - [c141]Parisa Kordjamshidi, Dan Roth, Kristian Kersting:
Systems AI: A Declarative Learning Based Programming Perspective. IJCAI 2018: 5464-5471 - [c140]Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan:
Structure Learning for Relational Logistic Regression: An Ensemble Approach. KR 2018: 661-662 - [i32]Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste:
Lifted Filtering via Exchangeable Decomposition. CoRR abs/1801.10495 (2018) - [i31]Patrick Schramowski, Christian Bauckhage, Kristian Kersting:
Neural Conditional Gradients. CoRR abs/1803.04300 (2018) - [i30]Stefano Teso, Kristian Kersting:
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users. CoRR abs/1805.08578 (2018) - [i29]Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani:
Probabilistic Deep Learning using Random Sum-Product Networks. CoRR abs/1806.01910 (2018) - [i28]Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera:
Automatic Bayesian Density Analysis. CoRR abs/1807.09306 (2018) - [i27]Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan:
Structure Learning for Relational Logistic Regression: An Ensemble Approach. CoRR abs/1808.02123 (2018) - [i26]Moritz Kulessa, Alejandro Molina, Carsten Binnig, Benjamin Hilprecht, Kristian Kersting:
Model-based Approximate Query Processing. CoRR abs/1811.06224 (2018) - 2017
- [j38]Kristian Kersting, Martin Mladenov, Pavel Tokmakov:
Relational linear programming. Artif. Intell. 244: 188-216 (2017) - [j37]Mehul Bhatt, Kristian Kersting:
Semantic Interpretation of Multi-Modal Human-Behaviour Data - Making Sense of Events, Activities, Processes. Künstliche Intell. 31(4): 317-320 (2017) - [j36]Youness Dehbi, Fabian Hadiji, Gerhard Gröger, Kristian Kersting, Lutz Plümer:
Statistical Relational Learning of Grammar Rules for 3D Building Reconstruction. Trans. GIS 21(1): 134-150 (2017) - [c139]Martin Mladenov, Vaishak Belle, Kristian Kersting:
The Symbolic Interior Point Method. AAAI 2017: 1199-1205 - [c138]Martin Mladenov, Leonard Kleinhans, Kristian Kersting:
Lifted Inference for Convex Quadratic Programs. AAAI 2017: 2350-2356 - [c137]Alejandro Molina, Sriraam Natarajan, Kristian Kersting:
Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions. AAAI 2017: 2357-2363 - [c136]Shuo Yang, Fabian Hadiji, Kristian Kersting, Shaun J. Grannis, Sriraam Natarajan:
Modeling heart procedures from EHRs: An application of exponential families. BIBM 2017: 491-497 - [c135]Iryna Gurevych, Christian M. Meyer, Carsten Binnig, Johannes Fürnkranz, Kristian Kersting, Stefan Roth, Edwin Simpson:
Interactive Data Analytics for the Humanities. CICLing (1) 2017: 527-549 - [c134]Christopher Morris, Kristian Kersting, Petra Mutzel:
Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs. ICDM 2017: 327-336 - [c133]Zhao Xu, Kristian Kersting, Lorenzo von Ritter:
Stochastic Online Anomaly Analysis for Streaming Time Series. IJCAI 2017: 3189-3195 - [c132]Navdeep Kaur, Gautam Kunapuli, Tushar Khot, Kristian Kersting, William Cohen, Sriraam Natarajan:
Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach. ILP 2017: 94-111 - [c131]Zhao Xu, Romain Vial, Kristian Kersting:
Graph Enhanced Memory Networks for Sentiment Analysis. ECML/PKDD (1) 2017: 374-389 - [e9]Gal Elidan, Kristian Kersting, Alexander T. Ihler:
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11-15, 2017. AUAI Press 2017 [contents] - [r6]Novi Quadrianto, Kristian Kersting, Zhao Xu:
Gaussian Process. Encyclopedia of Machine Learning and Data Mining 2017: 535-548 - [r5]Luc De Raedt, Kristian Kersting:
Statistical Relational Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1177-1187 - [r4]Scott Sanner, Kristian Kersting:
Symbolic Dynamic Programming. Encyclopedia of Machine Learning and Data Mining 2017: 1220-1228 - [i25]Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel:
A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels. CoRR abs/1703.00676 (2017) - [i24]Christopher Morris, Kristian Kersting, Petra Mutzel:
Global Weisfeiler-Lehman Graph Kernels. CoRR abs/1703.02379 (2017) - [i23]Alejandro Molina, Alexander Munteanu, Kristian Kersting:
Coresets for Dependency Networks. CoRR abs/1710.03285 (2017) - [i22]Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting:
Sum-Product Networks for Hybrid Domains. CoRR abs/1710.03297 (2017) - 2016
- [b3]Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole:
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2016 - [j35]Piotr Szymanski, Tomasz Kajdanowicz, Kristian Kersting:
How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification? Entropy 18(8): 282 (2016) - [j34]Christian Bauckhage, Kristian Kersting:
Collective Attention on the Web. Found. Trends Web Sci. 5(1-2): 1-136 (2016) - [j33]Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting:
Propagation kernels: efficient graph kernels from propagated information. Mach. Learn. 102(2): 209-245 (2016) - [c130]Martin Mladenov, Danny Heinrich, Leonard Kleinhans, Felix Gonsior, Kristian Kersting:
RELOOP: A Python-Embedded Declarative Language for Relational Optimization. AAAI Workshop: Declarative Learning Based Programming 2016 - [c129]Shuo Yang, Tushar Khot, Kristian Kersting, Sriraam Natarajan:
Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach. AAAI 2016: 2265-2271 - [c128]Sriraam Natarajan, Ameet Soni, Anurag Wazalwar, Dileep Viswanathan, Kristian Kersting:
Deep Distant Supervision: Learning Statistical Relational Models for Weak Supervision in Natural Language Extraction. Solving Large Scale Learning Tasks 2016: 331-345 - [c127]Christopher Morris, Nils M. Kriege, Kristian Kersting, Petra Mutzel:
Faster Kernels for Graphs with Continuous Attributes via Hashing. ICDM 2016: 1095-1100 - [c126]Joseph G. Taylor, Viktoriia Sharmanska, Kristian Kersting, David Weir, Novi Quadrianto:
Learning Using Unselected Features (LUFe). IJCAI 2016: 2060-2066 - [c125]Phillip Odom, Raksha Kumaraswamy, Kristian Kersting, Sriraam Natarajan:
Learning Through Advice-Seeking via Transfer. ILP 2016: 40-51 - [c124]Mayukh Das, Yuqing Wu, Tushar Khot, Kristian Kersting, Sriraam Natarajan:
Scaling Lifted Probabilistic Inference and Learning Via Graph Databases. SDM 2016: 738-746 - [p5]Kristian Kersting, Christian Bauckhage, Mirwaes Wahabzada, Anne-Katrin Mahlein, Ulrike Steiner, Erich-Christian Oerke, Christoph Römer, Lutz Plümer:
Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants. Computational Sustainability 2016: 99-120 - [e8]Jörg Lässig, Kristian Kersting, Katharina Morik:
Computational Sustainability. Studies in Computational Intelligence 645, Springer 2016, ISBN 978-3-319-31856-1 [contents] - [i21]Martin Mladenov, Vaishak Belle, Kristian Kersting:
The Symbolic Interior Point Method. CoRR abs/1605.08187 (2016) - [i20]Piotr Szymanski, Tomasz Kajdanowicz, Kristian Kersting:
How is a data-driven approach better than random choice in label space division for multi-label classification? CoRR abs/1606.02346 (2016) - [i19]Martin Mladenov, Leonard Kleinhans, Kristian Kersting:
Lifted Convex Quadratic Programming. CoRR abs/1606.04486 (2016) - [i18]Elena Erdmann, Karin Boczek, Lars Koppers, Gerret von Nordheim, Christian Pölitz, Alejandro Molina, Katharina Morik, Henrik Müller, Jörg Rahnenführer, Kristian Kersting:
Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage. CoRR abs/1606.05110 (2016) - [i17]Christopher Morris, Nils M. Kriege, Kristian Kersting, Petra Mutzel:
Faster Kernels for Graphs with Continuous Attributes via Hashing. CoRR abs/1610.00064 (2016) - 2015
- [j32]