Publications related to the Competence Center Machine Learning Rhine-Ruhr (ML2R)

2019

N. Piatkowski: Distributed Generative Modelling with Sub-Linear Communication Overhead. Proc. DMLE, 2019

N. Piatkowski: Hyper-Parameter-Free Generative Modelling with Deep Boltzmann Trees. ECML, 2019

G. Meschke, B.-T. Cao, A. Egorov, A. Saadallah, S. Freitag, K. Morik: Big data and simulation – A new approach for real-time TBM steering. Proc.  WTC, 2019.

A. Saadallah, A. Egorov, B.-T. Cao, S. Freitag, K. Morik G. Meschke: Active Learning for Accurate Settlement Prediction Using Numerical Simulations in Mechanized Tunneling. CIRP, 2019

M. Tavakol, S. Mair, S., K. Morik: HyperUCB: Hyperparameter Optimization using Contextual Bandits. Proc. ECML, 2019

C. Bauckhage, R. Sifa, T. Dong: Prototypes within Minimum Enclosing Balls. Proc. ICANN, 2019.

C. Bauckhage, N. Piatkowski, R. Sifa, D. Hecker, S. Wrobel: A QUBO Formulation of the k-Medoids Problem. Proc. LWDA, 2019.

T. Dong, C. Bauckhage, H. Jin, J. Li, O. Cremers, D. Speicher, A.B. Cremers, J. Zimmermann: Imposing Category Trees onto Word-Embeddings Using A Geometric Construction. Proc. ICLR, 2019.

T. Dong, Z. Wang, J. Li, C. Bauckhage, and A.B. Cremers: Triple Classification Using Regions and Fine-Grained Entity Typing. Proc. AAAI, 2019.

R. Fischer, N. Piatkowski, K. Morik: Parameter Sharing for Spatio-Temporal Process Models. Proc. LWDA, 2019.

V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. Proc. Workshop Representation Learning for NLP (RepL4NLP-2019) at ACL, 2019.

B. Kirsch, Z. Niyazova, S. Rüping, M. Mock: Noise Reduction in Distant Supervision for Relation Extraction using Probabilistic Soft Logic. Proc. Workshop Data Integration and Application (DINA) at ECML, 2019.

S. Mücke, N. Piatkowski, K. Morik: Learning Bit by Bit: Extracting the Essence of Machine Learning. Proc. LWDA, 2019.

S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. Proc. Workshop Decentralized Machine Learning at the Edge (DMLE) at ECML, 2019.

Ramamurthy, C. Bauckhage, R. Sifa, J. Schücker, S. Wrobel: Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures. Proc. ICANN. 2019.

R. Sifa, Yawar, R. Ramarmurthy, C. Bauckhage, K. Kersting: Matrix- and Tensor Factorization for Game Content Recommendation, KI – Künstliche Intelligenz (online first), 2019.

D. Trabold, T. Horvath, S. Wrobel: Effective Approximation of Parametrized Closure Systems over Transactional Data Streams. Machine Learning (online first), 2019.

P. Welke, T. Horvath, S. Wrobel: Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests. Machine Learning 108(7), 2019.

2018

K. Morik, Daten – wem gehören sie, wer speichert sie,wer darf auf sie zugreifen. In: K. Morik & W. Kraemer, Hg. Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen?, 2018

K. Morik, W. Kraemer (Hg.): Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen?, 2018

R. Schiffers, K. Morik, A. Schulze Struchtrup, P.-J. Honysz, J. Wortberg: Anomaly detection in injection molding process data based on unsupervised learning. Journal of Plastics Technology, 2018

L. Adilova, S. Giesselbach, and S. Rüping: Making Efficient Use of a Domain Expert’s Time in Relation Extraction. arXiv:1807.04687 [cs.LG], 2018.

C. Bauckhage, C. Ojeda, R. Sifa, and S. Wrobel: Adiabatic Quantum Computing for Kernel k=2 Means Clustering. Proc. LWDA, 2018.

C. Bauckhage, C. Ojeda, J. Schücker, R. Sifa, and S. Wrobel: Informed Machine Learning Through Functional Composition. Proc. LWDA, 2018.

M. Bunse, N. Piatkowski, and K. Morik: Towards a Unifying View on Deconvolution in Cherenkov Astronomy. Proc. LWDA, 2018.

S. Buschjäger and K. Morik: Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data. IEEE Trans. on Circuits and Systems 65-I(1), 2018.

M. Kamp, L. Adilova, J. Sicking, F. Hüger, P. Schlicht, T. Wirtz, and S. Wrobel: Efficient Decentralized Deep Learning by Dynamic Model Averaging. arXiv:1807.03210 [cs.LG], 2018.

B. Kirsch, S. Giesselbach, D. Knodt, and S. Rüping: Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring. In: G. Leventakis and M. Haberfeld (eds) Community-Oriented Policing and Technological Innovations, Springer, 2018.

K. Morik, C. Bockermann, and S. Buschjäger: Big Data Science. KI 32(1), 2018.

S. Hess, N. Piatkowski, and K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. Proc. SIAM Int. Conf. on Data Mining (SDM), 2018.

R. Ramamurthy, C. Bauckhage, R. Sifa, and S. Wrobel: Policy Learning Using SPSA. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

E. Schubert, S. Hess, and K. Morik: The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. Proc. LWDA, 2018.

R. Sifa, D. Paurat, D. Trabold, and C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

P. Welke, T. Horvath, and S. Wrobel: Probabilistic frequent subtrees for efficient graph classification and retrieval. Machine Learning 107(11), 2018.

B. Wulff, J. Schücker, and C. Bauckhage: SPSA for Layer-Wise Training of Deep Networks. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

2017

C. Bauckhage: A Neural Network Implementation of Frank-Wolfe Optimization. Proc. Int. Conf. on Artificial Neural Networks (ICANN),

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, and S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. Proc. Int. Conf. on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017

S. Hess and K. Morik: C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD),

S. Hess, K. Morik, and N. Piatkowski: The PRIMPING routine – Tiling through proximal alternating linearized minimization. Data Mining and Knowledge Discovery 31(4), 2017.

T. Liebig, N. Piatkowski, C. Bockermann, K. Morik: Dynamic route planning with real-time traffic predictions. Information Systems 64, 2017.

L. Pfahler, K. Morik, F. Elwert, S. Tabti, and V. Krech: Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

R. Sifa and C. Bauckhage: Online k-Maxoids Clustering. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, and S. Wrobel: Co-Regularised Support Vector Regression. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2017.

2016

M. Neumann, R. Garnett, C. Bauckhage, and K. Kersting: Propagation kernels: efficient graph kernels from propagated information. Machine Learning 102(2), 2016.

N. Piatkowski and K. Morik: Stochastic Discrete Clenshaw-Curtis Quadrature. Proc. Int. Conf on Machine Learning (ICML), 2016.

N. Piatkowski, S. Lee, and K. Morik: Integer undirected graphical models for resource-constrained systems. Neurocomputing 173, 2016.

C. Pölitz, W. Duivesteijn, and K. Morik: Interpretable domain adaptation via optimization over the Stiefel manifold. Machine Learning 104(2-3), 2016.

R. Sifa, S. Srikanth, A. Drachen, C. Ojeda, and C. Bauckhage: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning. Proc. IEEE Int Conf. on Computational Intelligence and Games (CIG), 2016.

2019

2019

N. Piatkowski: Distributed Generative Modelling with Sub-Linear Communication Overhead. Proc. DMLE, 2019

N. Piatkowski: Hyper-Parameter-Free Generative Modelling with Deep Boltzmann Trees. ECML, 2019

G. Meschke, B.-T. Cao, A. Egorov, A. Saadallah, S. Freitag, K. Morik: Big data and simulation – A new approach for real-time TBM steering. Proc.  WTC, 2019.

A. Saadallah, A. Egorov, B.-T. Cao, S. Freitag, K. Morik G. Meschke: Active Learning for Accurate Settlement Prediction Using Numerical Simulations in Mechanized Tunneling. CIRP, 2019

M. Tavakol, S. Mair, S., K. Morik: HyperUCB: Hyperparameter Optimization using Contextual Bandits. Proc. ECML, 2019

C. Bauckhage, R. Sifa, T. Dong: Prototypes within Minimum Enclosing Balls. Proc. ICANN, 2019.

C. Bauckhage, N. Piatkowski, R. Sifa, D. Hecker, S. Wrobel: A QUBO Formulation of the k-Medoids Problem. Proc. LWDA, 2019.

T. Dong, C. Bauckhage, H. Jin, J. Li, O. Cremers, D. Speicher, A.B. Cremers, J. Zimmermann: Imposing Category Trees onto Word-Embeddings Using A Geometric Construction. Proc. ICLR, 2019.

T. Dong, Z. Wang, J. Li, C. Bauckhage, and A.B. Cremers: Triple Classification Using Regions and Fine-Grained Entity Typing. Proc. AAAI, 2019.

R. Fischer, N. Piatkowski, K. Morik: Parameter Sharing for Spatio-Temporal Process Models. Proc. LWDA, 2019.

V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. Proc. Workshop Representation Learning for NLP (RepL4NLP-2019) at ACL, 2019.

B. Kirsch, Z. Niyazova, S. Rüping, M. Mock: Noise Reduction in Distant Supervision for Relation Extraction using Probabilistic Soft Logic. Proc. Workshop Data Integration and Application (DINA) at ECML, 2019.

S. Mücke, N. Piatkowski, K. Morik: Learning Bit by Bit: Extracting the Essence of Machine Learning. Proc. LWDA, 2019.

S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. Proc. Workshop Decentralized Machine Learning at the Edge (DMLE) at ECML, 2019.

Ramamurthy, C. Bauckhage, R. Sifa, J. Schücker, S. Wrobel: Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures. Proc. ICANN. 2019.

R. Sifa, Yawar, R. Ramarmurthy, C. Bauckhage, K. Kersting: Matrix- and Tensor Factorization for Game Content Recommendation, KI – Künstliche Intelligenz (online first), 2019.

D. Trabold, T. Horvath, S. Wrobel: Effective Approximation of Parametrized Closure Systems over Transactional Data Streams. Machine Learning (online first), 2019.

P. Welke, T. Horvath, S. Wrobel: Probabilistic and Exact Frequent Subtree Mining in Graphs Beyond Forests. Machine Learning 108(7), 2019.

2018

2018

K. Morik, Daten – wem gehören sie, wer speichert sie,wer darf auf sie zugreifen. In: K. Morik & W. Kraemer, Hg. Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen?, 2018

K. Morik, W. Kraemer (Hg.): Daten – wem gehören sie, wer speichert sie, wer darf auf sie zugreifen?, 2018

R. Schiffers, K. Morik, A. Schulze Struchtrup, P.-J. Honysz, J. Wortberg: Anomaly detection in injection molding process data based on unsupervised learning. Journal of Plastics Technology, 2018

L. Adilova, S. Giesselbach, and S. Rüping: Making Efficient Use of a Domain Expert’s Time in Relation Extraction. arXiv:1807.04687 [cs.LG], 2018.

C. Bauckhage, C. Ojeda, R. Sifa, and S. Wrobel: Adiabatic Quantum Computing for Kernel k=2 Means Clustering. Proc. LWDA, 2018.

C. Bauckhage, C. Ojeda, J. Schücker, R. Sifa, and S. Wrobel: Informed Machine Learning Through Functional Composition. Proc. LWDA, 2018.

M. Bunse, N. Piatkowski, and K. Morik: Towards a Unifying View on Deconvolution in Cherenkov Astronomy. Proc. LWDA, 2018.

S. Buschjäger and K. Morik: Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data. IEEE Trans. on Circuits and Systems 65-I(1), 2018.

M. Kamp, L. Adilova, J. Sicking, F. Hüger, P. Schlicht, T. Wirtz, and S. Wrobel: Efficient Decentralized Deep Learning by Dynamic Model Averaging. arXiv:1807.03210 [cs.LG], 2018.

B. Kirsch, S. Giesselbach, D. Knodt, and S. Rüping: Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring. In: G. Leventakis and M. Haberfeld (eds) Community-Oriented Policing and Technological Innovations, Springer, 2018.

K. Morik, C. Bockermann, and S. Buschjäger: Big Data Science. KI 32(1), 2018.

S. Hess, N. Piatkowski, and K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. Proc. SIAM Int. Conf. on Data Mining (SDM), 2018.

R. Ramamurthy, C. Bauckhage, R. Sifa, and S. Wrobel: Policy Learning Using SPSA. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

E. Schubert, S. Hess, and K. Morik: The Relationship of DBSCAN to Matrix Factorization and Spectral Clustering. Proc. LWDA, 2018.

R. Sifa, D. Paurat, D. Trabold, and C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

P. Welke, T. Horvath, and S. Wrobel: Probabilistic frequent subtrees for efficient graph classification and retrieval. Machine Learning 107(11), 2018.

B. Wulff, J. Schücker, and C. Bauckhage: SPSA for Layer-Wise Training of Deep Networks. Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2018.

2017

2017

C. Bauckhage: A Neural Network Implementation of Frank-Wolfe Optimization. Proc. Int. Conf. on Artificial Neural Networks (ICANN),

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, and S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. Proc. Int. Conf. on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017

S. Hess and K. Morik: C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD),

S. Hess, K. Morik, and N. Piatkowski: The PRIMPING routine – Tiling through proximal alternating linearized minimization. Data Mining and Knowledge Discovery 31(4), 2017.

T. Liebig, N. Piatkowski, C. Bockermann, K. Morik: Dynamic route planning with real-time traffic predictions. Information Systems 64, 2017.

L. Pfahler, K. Morik, F. Elwert, S. Tabti, and V. Krech: Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

R. Sifa and C. Bauckhage: Online k-Maxoids Clustering. Proc. IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 2017.

K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, and S. Wrobel: Co-Regularised Support Vector Regression. Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2017.

2016

2016

M. Neumann, R. Garnett, C. Bauckhage, and K. Kersting: Propagation kernels: efficient graph kernels from propagated information. Machine Learning 102(2), 2016.

N. Piatkowski and K. Morik: Stochastic Discrete Clenshaw-Curtis Quadrature. Proc. Int. Conf on Machine Learning (ICML), 2016.

N. Piatkowski, S. Lee, and K. Morik: Integer undirected graphical models for resource-constrained systems. Neurocomputing 173, 2016.

C. Pölitz, W. Duivesteijn, and K. Morik: Interpretable domain adaptation via optimization over the Stiefel manifold. Machine Learning 104(2-3), 2016.

R. Sifa, S. Srikanth, A. Drachen, C. Ojeda, and C. Bauckhage: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning. Proc. IEEE Int Conf. on Computational Intelligence and Games (CIG), 2016.