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

2021

L. von Rueden, T. Wirtz, F. Hueger, J. D. Schneider, N. Piatkowski, C. Bauckhage: Street-Map Based Validation of Semantic Segmentation in Autonomous Driving. ICPR, 2021.

P. Welke, F. Alkhoury, C. Bauckhage, S. Wrobel: Decision Snippet Features. ICPR. 2021.

C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Stochastic-Path Examples Generation for Explanations. ICPR, 2021.

C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2021.

C. Ojeda, K. Cvejoski, J. Schuecker, B. Georgiev, C. Bauckhage, R. Sanchez: An Adversarial Approach towards Queuing Systems Modeling. AAAI, 2021.

V. Olari, K. Cvejoski, Ø. Eide: Introduction to machine learning with robots and playful learning. AAAI, 2021.

J. Kalofolias, P. Welke, J. Vreeken: SUSAN: The Structural Similarity Random Walk Kernel. SIAM Data Mining, 2021.

M. Pielka, R. Sifa, L. P. Hillebrand, D. Biesner, R. Ramamurthy, A.Ladi, C. Bauckhage: Tackling Contradiction Detection in German Using Machine Translation and End-to-End Recurrent Neural Networks. ICPR, 2021.

S. Hänold, N. Schlee, D. Antweiler, K. Beckh: Die Nachvollziehbarkeit von KI-Anwendungen in der Medizin – eine Betrachtung aus juristischer Perspektive mit Beispielszenarien. In: Medizinrecht, 2021.

V. Gupta, K. Beckh, S. Giesselbach, D. Wegener, T. Wirtz: Supporting verification of news articles with automated search for semantically similar articles. ROMCIR, 2021.
L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning Using Non-Negative Tensor DEDICOM. In: Machine Learning and Knowledge Extraction, 2021.

2020

K. Cvejoski, R. J. Sanchez, B. Georgiev, J.Schuecker, C. Bauckhage, C. Ojeda: Recurrent Point Processes for Dynamic Review Models. WICRS Workshop at AAAI, 2020.

D. Biesner, R. Ramamurthy, M. Lübbering, B. Fürst, H. Ismail, L. Hillebrand, A. Ladi, M. Pielka, R. Stenzel, T. Khameneh, V. Krapp, I. Huseynov, J. Schlums, U. Stoll, U. Warning, B. Kliem, C. Bauckhage, R. Sifa: Leveraging Contextual Text Representations for Anonymizing German Financial Documents. AAAI Workshop on Knowledge Discovery from Unstructured Data in Financial Services at KDF, 2020.

O. Urbann, S. Camphausen, A. Moos, I. Schwarz, S. Kerner, M. Otten: A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems. C4ML Workshop at CGO, 2020.

M. Nanni, A. Gennady, A.-L. Barabasi, C. A. Boldrini, F. Bonchi, C. Cattuto, F. Chiaromonte, G. Commande, M. Conti, M. Cote, F. Dignum, V. Dignum, J. Domingo-Ferrer, P. Ferragina, F. Giannotti, R. Guidotti, D. Helbing, K. Kaski, J. Kertesz, S. Lehmann, B. Lepri, P. Lukowicz, S. Matwin, J. Megias, D. Megias, A. Monreale, K. Morik, N. Oliver, A. Passarella, A. Passerini, D. Pedreschi, A. Pentland, F. Pianesi, F. Pratesi, S. Rinzivillo, S. Ruggieri, A. Siebes, V. Torra, R. Trasarti, J. van der Hoven, A. Vespignani: Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. Transactions on Data Privacy, Vol. 13. April 2020, S. 61 – 66.

L. von Rueden, S. Mayer, R. Sifa, C. Bauckhage, J. Garcke: Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions. IDA, 2020.

C. Bauckhage, R.Sifa, S. Wrobel: Adiabatic Quantum Computing for Max-Sum Diversication. SDM, 2020.

R. Sifa: DESICOM as Metaheuristic Search. LION, 2020.

M. Cekic, B. Georgiev, M. Mukherjee: Polyhedral billiards, eigenfunction concentration and almost periodic control. In: Communications in Mathematical Physics, 2020.

C. Bauckhage, R. Sanchez, R. Sifa: Problem Solving with Hopfield Networks and Adiabatic Quantum Computing. IJCNN, 2020.

K. Cvejoski, C. Ojeda, B. Georgiev, C. Bauckhage, R. J. Sanchez: Recurrent Point Review Models. IJCNN, 2020.

A. Kiwan, S. Gisselbach, S. Rüping: Incorporating Knowledge Bases into SciBERT and BioBERT pre-trainedlanguage models. SciNLP Workshop at AKBC, 2020.

B. Sliwa, N. Piatkowski, C. Wietfeld: LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations. ICC, 2020.

B. Sliwa, N. Piatkowski, C. Wietfeld: The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting. IEEE Internet of Things Journal, 2020.

F. Finkeldey, A. Saadallah, P. Widerkehr, K. Morik: Real-time prediction of process forces in milling operations using synchronized data fusion of simulation and sensor data. In: Engineering Applications of Artificial Intelligence, Elsevier, 2020.

L. von Rueden, T. Wirtz, F. Hueger, J. D. Schneider, C. Bauckhage: Towards Map-Based Validation of Semantic Segmentation Masks. AIAD Workshop at ICML, 2020.

K. Y. Chai, J. Stenzel, J. Jost: Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN. IRCE, 2020.

L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM. CD-MAKE, 2020.

L. Pfahler, K. Morik: Semantic Search in Millions of Equations. KDD, 2020.

P. Welke, F. Seiffarth, M. Kamp, S. Wrobel: HOPS: Probabilistic Subtree Mining for Small and Large Graphs. KDD, 2020.

B. Kirsch, S. Giesselbach, T. Schmude, S.Rüping: Probabilistic Soft Logic to Improve Information Extraction in the Legal Domain. LWDA, 2020.

R. Fischer, M. Jakobs, S. Mücke, K. Morik: Solving Abstract Reasoning Tasks with Grammatical Evolution. LWDA, 2020.

J. Kindermann, K. Beckh: Fusing Multi-label Classification and Semantic Tagging. LWDA, 2020.

S. Buschjäger, L. Pfahler, J. Buss, K. Morik, W. Rhode: On-Site Gamma-Hadron Separation with Deep Learning on FPGAs. ECML PKDD, 2020.

L. Heppe, M. Kamp, L. Adilova, N. Piatkowski, D. Heinrich, K. Morik: Resource-Constrained On-Device Learning by Dynamic Averaging. PDFL Workshop at ECML PKDD, 2020.

F. Seiffarth, T. Horvath, S. Wrobel: Maximum Margin Separations in Finite Closure Systems. ECML PKDD, 2020.

B. Georgiev, G. Angelov: On Learning a Control System without Continuous Feedback. ESANN, 2020.

B. Georgiev, L. Franken: Explorations in Quantum Neural Networks withIntermediate Measurements. ESANN, 2020.

O. Urbann, S. Camphausen, A. Moos, I. Schwarz, S. Kerner, M. Otten: A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems. IEMTRONICS Workshop at IML, 2020.

R. Fischer, N. Piatkowski, C. Pelletier, G. Webb, F. Petitjean, K. Morik: No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image. DSAA, 2020.

C. Bauckhage, R. Sifa: Shells within Minimum Enclosing Balls. DSAA, 2020.

A. Saadallah, K. Morik: Active Sampling for Learning Interpretable Surrogate Machine Learning Models. DSAA, 2020.

A. Mehler, W. Hemati, P. Welke, M. Konca, T. Uslu: Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages. Frontiers in Education, 2020.

D. Weichert, K. Morik, M. Bunse, A. Kister: Optimal Probabilistic Classification in Active Class Selection. ICDM, 2020.

D. Antweiler, P. Welke: Temporal Graph Analysis for Outbreak Pattern Detection in COVID-19 Contact Tracing Networks. Machine Learning for Public Health Workshop at NeurIPS, 2020.

C. Bauckhage, R. Ramamurthy, R. Sifa: Hopfield Networks for Vector Quantization. ICANN, 2020.

R. Ramamurthy, R. Sifa, M. Lübbering, C. Bauckhage: Guided Reinforcement Learning via Sequence Learning. ICANN, 2020.

M. Lübbering, R. Ramamurthy, M. Gebauer, T. Bell, R. Sifa, C. Bauckhage: From Imbalanced Classification to Supervised Outlier Detection Problems. ICANN, 2020.

S. Buschjäger, P.-J. Honysz, K. Morik: Randomized Outlier Detection with Trees. In: International Journal of Data Science and Analytics, 2020.

L. Franken, B. Georgiev, S. Muecke, M. Wolter, N. Piatkowski, C. Bauckhage: Gradient-free quantum optimization on NISQ devices. arxiv preprint, 2020.

M. Elahi, R. Hosseini, M. H. Rimaz, F. B. Moghaddam, C. Trattner: Visually-Aware Video Recommendation in the Cold Start. ACM, 2020.

X. Han, T. Grubenmann, R. Cheng, S. C. Wong, X. Li, W. Sun: Traffic Incident Detection: A Trajectory-based Approach. IEEE, 2020.

A. Sadeghi, D. Graux, H. S. Yazdi, J. Lehmann: MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs. ECAI, 2020.

H. Zafar, M. Tavakol, J. Lehmann: Distantly Supervised Question Parsing. ECAI, 2020.

F. A. Musyaffa, M. Vidal, F. Orlandi, J. Lehmann, H. Jabeen: IOTA: Interlinking of heterogeneous multilingual open fiscal DaTA. ACM, 2020.

M. Cremaschi, F. De Paoli, A. Rula, B. Spahiu: A fully automated approach to a complete Semantic Table Interpretation. In: Future Generation Computer Systems, 2020.

D. Tomaszuk, R. Angles, H. Thakkar: PGO: Describing Property Graphs in RDF. IEEE, 2020.

S. Payrosangari, A. Sadeghi, D. Graux, J. Lehmann: Meta-hyperband: Hyperparameter Optimization with Meta-learning and Coarse-to-Fine. IDEAL, 2020.

M. Nayyeri, X. Zhou, S. Vahdati, R. Izanloo, H. S. Yazdi, J. Lehmann: Let the Margin SlidE for Knowledge Graph Embeddings via a Correntropy Objective Function. IEEE, 2020.

H. Jabeen, D. Graux, G. Sejdiu: Scalable Knowledge Graph Processing Using SANSA. In: Knowledge Graphs and Big Data Processing, 2020.

R. Nedelchev, R. Usbeck, J. Lehmann: Treating Dialogue Quality Evaluation as an Anomaly Detection Problem. LREC, 2020.

S. R. Bader, I. Grangel-González, P. Nanjappa, M. Vidal, M. Maleshkova: A Knowledge Graph for Industry 4.0. ESWC, 2020.

E. Kacupaj, H. Zafar, J. Lehmann, M. Maleshkova: VQuAnDa: Verbalization QUestion ANswering DAtaset. ESWC, 2020.

M. Nayyeri, S. Vahdati, X. Zhou, H. S. Yazdi, J. Lehmann: Embedding-Based Recommendations on Scholarly Knowledge Graphs. ESWC, 2020.

M. Nayyeri, C. Xu, S. Vahdati, N. Vassilyeva, E. Sallinger, H. S. Yazdi, J. Lehmann: Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them. ISWC, 2020.

C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann: Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding. ISWC, 2020.

T. Grubenmann, R. C. K. Cheng, L. V. S. Lakshmanan: TSA: A Truthful Mechanism for Social Advertising. WSDM, 2020.

I. O. Mulang, K. Singh, A. Vyas, S. Shekarpour, M. Vidal, S. Auer: Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking. WISE, 2020.

Z. Say, S. Fathalla, S. Vahdati, J. Lehmann, S. Auer: Ontology Design for Pharmaceutical Research Outcomes. TPDL, 2020.

M. Elias, M. R. Tavakoli, S. Lohmann, G. Kismihók, S. Auer: An OER Recommender System Supporting Accessibility Requirements. ASSETS, 2020.

H. Jabeen, E. Haziiev, G. Sejdiu, J. Lehmann: DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data. IEEE, 2020.

H. Thakkar, R. Angles, M. Rodriguez, S. Mallette, J. Lehmann: Let’s build Bridges, not Walls: SPARQL Querying of TinkerPop Graph Databases with Sparql-Gremlin. IEEE, 2020.

R. Angles, H. Thakkar, D. Tomaszuk: Mapping RDF Databases to Property Graph Databases. IEEE, 2020.

H. Jabeen, J. Weinz, J. Lehmann: AutoChef: Automated Generation of Cooking Recipes. CEC, 2020.

H. Jabeen: Big Data Outlook, Tools, and Architectures. In: Knowledge Graphs and Big Data Processing, 2020.

M. Tasnim, D. Collarana, D. Graux, M. Vidal: Context-Based Entity Matching for Big Data. In: Knowledge Graphs and Big Data Processing, 2020.

I. O. Mulang’, K. Singh, C. Prabhu, A. Nadgeri, J. Hoffart, J. Lehmann: Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models. CIKM, 2020.

J. Armitage, E. Kacupaj, G. Tahmasebzadeh, Swati, M. Maleshkova, R. Ewerth, J. Lehmann: MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities. CIKM, 2020.

M. Nayyeri, M. M. Alam, J. Lehmann, S. Vahdati: 3D Learning and Reasoning in Link Prediction Over Knowledge Graphs. IEEE, 2020.

A. Say, S. Fathalla, S. Vahdati, J. Lehmann S. Auer: Semantic Representation of Physics Research Data. IC3K, 2020.

J. Armitage, S. Thakur, R. Tripathi, J. Lehmann, M. Maleshkova: Training Multimodal Systems for Classification with Multiple Objectives. ESWC, 2020.

M. Galkin, P. Trivedi, G. Maheshwari, R. Usbeck, J. Lehmann: Message Passing for Hyper-Relational Knowledge Graphs. EMNLP, 2020.

C. Xu, M. Nayyeri, Y. Chen, J. Lehmann: Knowledge Graph Embeddings in Geometric Algebras. COLING, 2020.

C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann: TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation. COLING, 2020.

R. Nedelchev, J. Lehmann, R. Usbeck: Language Model Transformers as Evaluators for Open-domain Dialogues. COLING, 2020.

E. Iglesias, S. Jozashoori, D. Chaves-Fraga, D. Collarana, M. Vidal: SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs. CIKM, 2020.

A. Rivas, I. Grangel-González, D. Collarana, J. Lehmann, M. Vidal: Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings. DEXA, 2020.

D. Banerjee, D. Chaudhuri, M. Dubey, J. Lehmann: PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs. ISWC, 2020.

C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Stochastic-Path Examples Generation for Explanations. ICPR, 2020.

C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2020.

2019

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

S. Hess, W. Duivesteijn, K.Morik, P.-J. Honysz: The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. AAAI, 2019.

S. Buschjäger, T. Liebig, K. Morik: Gaussian Model Trees for Traffic Imputation. ICPRAM, 2019.

A. Saadallah, N. Piatkowski, F. Finkeldey, P. Wiederkehr, K. Morik: Learning Ensembles in the Presence of Imbalanced Classes. ICPRAM, 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. WTC, 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. ICLR, 2019.

V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. RepL4NLP Workshop at ACL, 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.

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

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

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

S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. DMLE Workshop at ECML, 2019.

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

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

L. Pfahler, J. Schill, K. Morik: The Search for Equations – Learning to Identify Similarities between Mathematical Expressions. ECML PKDD, 2019.

P. Welke, T. Schulz: On the Necessity of Graph Kernel Baselines. Graph Embedding and Mining Workhop at ECML PKDD, 2019.

F. Seiffarth, T. Horvath, S. Wrobel: Maximal Closed Set and Half-Space Separations in Finite Closure Systems. ECML PKDD, 2019.

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

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

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

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

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

J. HaiLong, H. Lei, L. Juanzi, T. Dong: Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks. EMNLP, 2019.

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

S. Kerner, J. Leveling , M. Otten, O. Urbann, M. Vogel, L. Weickhmann: Anwendungsfelder von künstlicher Intelligenz in Industrie 4.0 Systemen. Handbuch Industrie 4.0, 2019.

P. Tözün, H. Kotthaus: Scheduling Data-Intensive Tasks on Heterogeneous Many Cores. IEEE, 2019.

H. Kotthaus, L. Schönberger, A. Lang, J. Chen, P. Marwedel: Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently?. SCOPES, 2019.

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.

S. Hess, N. Piatkowski, K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. SIAM SDM, 2018.

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

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

S. Hao, X. Ma, T. Dong, A. B. Cremers, C. Chun: An Assertion Framework for Mobile Robotic Programming with Spatial Reasoning. COMPSAC, 2018.

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

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

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

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

R. Ramamurthy, C. Bauckhage, R. Sifa, S. Wrobel: Policy Learning Using SPSA. ICANN, 2018.

R. Sifa, D. Paurat, D. Trabold, C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. ICANN, 2018.

B. Wulff, J. Schücker, C. Bauckhage: SPSA for Layer-Wise Training of Deep Networks. ICANN, 2018.

Y. Cao, L. Hou, J. Li, Z. Liu, C. Li, X. Chen, T. Dong: Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision. EMNLP, 2018.

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

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

S. Giesselbach, K. Ullrich, M. Kamp, D. Paurat, T. Gärtner: Corresponding Projections for Orphan Screening. ML4H Workshop at NeurIPS, 2018.

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

B. Kirsch, S. Giesselbach, D. Knodt, 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, S. Buschjäger: Big Data Science. KI 32(1), 2018.

2017

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

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

K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, S. Wrobel: Co-Regularised Support Vector Regression. ECML PKDD, 2017.

L. Pfahler, K. Morik, F. Elwert, S. Tabti, V. Krech: Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization. IEEE DSAA, 2017.

R. Sifa, C. Bauckhage: Online k-Maxoids Clustering. IEEE DSAA, 2017.

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. EMMCVPR, 2017.

H. Kotthaus, J. Richter, A. Lang, J. Thomas, B. Bischl, P. Marwedel, J. Rahnenführer, M. Lang: RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. LION, 2017.

2016

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

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

N. Piatkowski, K. Morik: Stochastic Discrete Clenshaw-Curtis Quadrature. ICML, 2016.

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

R. Sifa, S. Srikanth, A. Drachen, C. Ojeda, C. Bauckhage: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning. IEEE CIG, 2016.

J. Richter, H. Kotthaus, B. Bischl, P. Marwedel, J. Rahnenführer, M. Lang: Faster Model-Based Optimization through Resource-Aware Scheduling Strategies. LION, 2016.

2021

2021

L. von Rueden, T. Wirtz, F. Hueger, J. D. Schneider, N. Piatkowski, C. Bauckhage: Street-Map Based Validation of Semantic Segmentation in Autonomous Driving. ICPR, 2021.

P. Welke, F. Alkhoury, C. Bauckhage, S. Wrobel: Decision Snippet Features. ICPR. 2021.

C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Stochastic-Path Examples Generation for Explanations. ICPR, 2021.

C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2021.

C. Ojeda, K. Cvejoski, J. Schuecker, B. Georgiev, C. Bauckhage, R. Sanchez: An Adversarial Approach towards Queuing Systems Modeling. AAAI, 2021.

V. Olari, K. Cvejoski, Ø. Eide: Introduction to machine learning with robots and playful learning. AAAI, 2021.

J. Kalofolias, P. Welke, J. Vreeken: SUSAN: The Structural Similarity Random Walk Kernel. SIAM Data Mining, 2021.

M. Pielka, R. Sifa, L. P. Hillebrand, D. Biesner, R. Ramamurthy, A.Ladi, C. Bauckhage: Tackling Contradiction Detection in German Using Machine Translation and End-to-End Recurrent Neural Networks. ICPR, 2021.

S. Hänold, N. Schlee, D. Antweiler, K. Beckh: Die Nachvollziehbarkeit von KI-Anwendungen in der Medizin – eine Betrachtung aus juristischer Perspektive mit Beispielszenarien. In: Medizinrecht, 2021.

V. Gupta, K. Beckh, S. Giesselbach, D. Wegener, T. Wirtz: Supporting verification of news articles with automated search for semantically similar articles. ROMCIR, 2021.
L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning Using Non-Negative Tensor DEDICOM. In: Machine Learning and Knowledge Extraction, 2021.
2020

2020

K. Cvejoski, R. J. Sanchez, B. Georgiev, J.Schuecker, C. Bauckhage, C. Ojeda: Recurrent Point Processes for Dynamic Review Models. WICRS Workshop at AAAI, 2020.

D. Biesner, R. Ramamurthy, M. Lübbering, B. Fürst, H. Ismail, L. Hillebrand, A. Ladi, M. Pielka, R. Stenzel, T. Khameneh, V. Krapp, I. Huseynov, J. Schlums, U. Stoll, U. Warning, B. Kliem, C. Bauckhage, R. Sifa: Leveraging Contextual Text Representations for Anonymizing German Financial Documents. AAAI Workshop on Knowledge Discovery from Unstructured Data in Financial Services at KDF, 2020.

O. Urbann, S. Camphausen, A. Moos, I. Schwarz, S. Kerner, M. Otten: A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems. C4ML Workshop at CGO, 2020.

M. Nanni, A. Gennady, A.-L. Barabasi, C. A. Boldrini, F. Bonchi, C. Cattuto, F. Chiaromonte, G. Commande, M. Conti, M. Cote, F. Dignum, V. Dignum, J. Domingo-Ferrer, P. Ferragina, F. Giannotti, R. Guidotti, D. Helbing, K. Kaski, J. Kertesz, S. Lehmann, B. Lepri, P. Lukowicz, S. Matwin, J. Megias, D. Megias, A. Monreale, K. Morik, N. Oliver, A. Passarella, A. Passerini, D. Pedreschi, A. Pentland, F. Pianesi, F. Pratesi, S. Rinzivillo, S. Ruggieri, A. Siebes, V. Torra, R. Trasarti, J. van der Hoven, A. Vespignani: Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. Transactions on Data Privacy, Vol. 13. April 2020, S. 61 – 66.

L. von Rueden, S. Mayer, R. Sifa, C. Bauckhage, J. Garcke: Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions. IDA, 2020.

C. Bauckhage, R.Sifa, S. Wrobel: Adiabatic Quantum Computing for Max-Sum Diversication. SDM, 2020.

R. Sifa: DESICOM as Metaheuristic Search. LION, 2020.

M. Cekic, B. Georgiev, M. Mukherjee: Polyhedral billiards, eigenfunction concentration and almost periodic control. In: Communications in Mathematical Physics, 2020.

C. Bauckhage, R. Sanchez, R. Sifa: Problem Solving with Hopfield Networks and Adiabatic Quantum Computing. IJCNN, 2020.

K. Cvejoski, C. Ojeda, B. Georgiev, C. Bauckhage, R. J. Sanchez: Recurrent Point Review Models. IJCNN, 2020.

A. Kiwan, S. Gisselbach, S. Rüping: Incorporating Knowledge Bases into SciBERT and BioBERT pre-trainedlanguage models. SciNLP Workshop at AKBC, 2020.

B. Sliwa, N. Piatkowski, C. Wietfeld: LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations. ICC, 2020.

B. Sliwa, N. Piatkowski, C. Wietfeld: The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting. IEEE Internet of Things Journal, 2020.

F. Finkeldey, A. Saadallah, P. Widerkehr, K. Morik: Real-time prediction of process forces in milling operations using synchronized data fusion of simulation and sensor data. In: Engineering Applications of Artificial Intelligence, Elsevier, 2020.

L. von Rueden, T. Wirtz, F. Hueger, J. D. Schneider, C. Bauckhage: Towards Map-Based Validation of Semantic Segmentation Masks. AIAD Workshop at ICML, 2020.

K. Y. Chai, J. Stenzel, J. Jost: Generation, Classification and Segmentation of Point Clouds in Logistic Context with PointNet++ and DGCNN. IRCE, 2020.

L. Hillebrand, D. Biesner, C. Bauckhage, R. Sifa: Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM. CD-MAKE, 2020.

L. Pfahler, K. Morik: Semantic Search in Millions of Equations. KDD, 2020.

P. Welke, F. Seiffarth, M. Kamp, S. Wrobel: HOPS: Probabilistic Subtree Mining for Small and Large Graphs. KDD, 2020.

B. Kirsch, S. Giesselbach, T. Schmude, S.Rüping: Probabilistic Soft Logic to Improve Information Extraction in the Legal Domain. LWDA, 2020.

R. Fischer, M. Jakobs, S. Mücke, K. Morik: Solving Abstract Reasoning Tasks with Grammatical Evolution. LWDA, 2020.

J. Kindermann, K. Beckh: Fusing Multi-label Classification and Semantic Tagging. LWDA, 2020.

S. Buschjäger, L. Pfahler, J. Buss, K. Morik, W. Rhode: On-Site Gamma-Hadron Separation with Deep Learning on FPGAs. ECML PKDD, 2020.

L. Heppe, M. Kamp, L. Adilova, N. Piatkowski, D. Heinrich, K. Morik: Resource-Constrained On-Device Learning by Dynamic Averaging. PDFL Workshop at ECML PKDD, 2020.

F. Seiffarth, T. Horvath, S. Wrobel: Maximum Margin Separations in Finite Closure Systems. ECML PKDD, 2020.

B. Georgiev, G. Angelov: On Learning a Control System without Continuous Feedback. ESANN, 2020.

B. Georgiev, L. Franken: Explorations in Quantum Neural Networks withIntermediate Measurements. ESANN, 2020.

O. Urbann, S. Camphausen, A. Moos, I. Schwarz, S. Kerner, M. Otten: A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems. IEMTRONICS Workshop at IML, 2020.

R. Fischer, N. Piatkowski, C. Pelletier, G. Webb, F. Petitjean, K. Morik: No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image. DSAA, 2020.

C. Bauckhage, R. Sifa: Shells within Minimum Enclosing Balls. DSAA, 2020.

A. Saadallah, K. Morik: Active Sampling for Learning Interpretable Surrogate Machine Learning Models. DSAA, 2020.

A. Mehler, W. Hemati, P. Welke, M. Konca, T. Uslu: Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages. Frontiers in Education, 2020.

D. Weichert, K. Morik, M. Bunse, A. Kister: Optimal Probabilistic Classification in Active Class Selection. ICDM, 2020.

D. Antweiler, P. Welke: Temporal Graph Analysis for Outbreak Pattern Detection in COVID-19 Contact Tracing Networks. Machine Learning for Public Health Workshop at NeurIPS, 2020.

C. Bauckhage, R. Ramamurthy, R. Sifa: Hopfield Networks for Vector Quantization. ICANN, 2020.

R. Ramamurthy, R. Sifa, M. Lübbering, C. Bauckhage: Guided Reinforcement Learning via Sequence Learning. ICANN, 2020.

M. Lübbering, R. Ramamurthy, M. Gebauer, T. Bell, R. Sifa, C. Bauckhage: From Imbalanced Classification to Supervised Outlier Detection Problems. ICANN, 2020.

S. Buschjäger, P.-J. Honysz, K. Morik: Randomized Outlier Detection with Trees. In: International Journal of Data Science and Analytics, 2020.

L. Franken, B. Georgiev, S. Muecke, M. Wolter, N. Piatkowski, C. Bauckhage: Gradient-free quantum optimization on NISQ devices. arxiv preprint, 2020.

M. Elahi, R. Hosseini, M. H. Rimaz, F. B. Moghaddam, C. Trattner: Visually-Aware Video Recommendation in the Cold Start. ACM, 2020.

X. Han, T. Grubenmann, R. Cheng, S. C. Wong, X. Li, W. Sun: Traffic Incident Detection: A Trajectory-based Approach. IEEE, 2020.

A. Sadeghi, D. Graux, H. S. Yazdi, J. Lehmann: MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs. ECAI, 2020.

H. Zafar, M. Tavakol, J. Lehmann: Distantly Supervised Question Parsing. ECAI, 2020.

F. A. Musyaffa, M. Vidal, F. Orlandi, J. Lehmann, H. Jabeen: IOTA: Interlinking of heterogeneous multilingual open fiscal DaTA. ACM, 2020.

M. Cremaschi, F. De Paoli, A. Rula, B. Spahiu: A fully automated approach to a complete Semantic Table Interpretation. In: Future Generation Computer Systems, 2020.

D. Tomaszuk, R. Angles, H. Thakkar: PGO: Describing Property Graphs in RDF. IEEE, 2020.

S. Payrosangari, A. Sadeghi, D. Graux, J. Lehmann: Meta-hyperband: Hyperparameter Optimization with Meta-learning and Coarse-to-Fine. IDEAL, 2020.

M. Nayyeri, X. Zhou, S. Vahdati, R. Izanloo, H. S. Yazdi, J. Lehmann: Let the Margin SlidE for Knowledge Graph Embeddings via a Correntropy Objective Function. IEEE, 2020.

H. Jabeen, D. Graux, G. Sejdiu: Scalable Knowledge Graph Processing Using SANSA. In: Knowledge Graphs and Big Data Processing, 2020.

R. Nedelchev, R. Usbeck, J. Lehmann: Treating Dialogue Quality Evaluation as an Anomaly Detection Problem. LREC, 2020.

S. R. Bader, I. Grangel-González, P. Nanjappa, M. Vidal, M. Maleshkova: A Knowledge Graph for Industry 4.0. ESWC, 2020.

E. Kacupaj, H. Zafar, J. Lehmann, M. Maleshkova: VQuAnDa: Verbalization QUestion ANswering DAtaset. ESWC, 2020.

M. Nayyeri, S. Vahdati, X. Zhou, H. S. Yazdi, J. Lehmann: Embedding-Based Recommendations on Scholarly Knowledge Graphs. ESWC, 2020.

M. Nayyeri, C. Xu, S. Vahdati, N. Vassilyeva, E. Sallinger, H. S. Yazdi, J. Lehmann: Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them. ISWC, 2020.

C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann: Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding. ISWC, 2020.

T. Grubenmann, R. C. K. Cheng, L. V. S. Lakshmanan: TSA: A Truthful Mechanism for Social Advertising. WSDM, 2020.

I. O. Mulang, K. Singh, A. Vyas, S. Shekarpour, M. Vidal, S. Auer: Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking. WISE, 2020.

Z. Say, S. Fathalla, S. Vahdati, J. Lehmann, S. Auer: Ontology Design for Pharmaceutical Research Outcomes. TPDL, 2020.

M. Elias, M. R. Tavakoli, S. Lohmann, G. Kismihók, S. Auer: An OER Recommender System Supporting Accessibility Requirements. ASSETS, 2020.

H. Jabeen, E. Haziiev, G. Sejdiu, J. Lehmann: DISE: A Distributed in-Memory SPARQL Processing Engine over Tensor Data. IEEE, 2020.

H. Thakkar, R. Angles, M. Rodriguez, S. Mallette, J. Lehmann: Let’s build Bridges, not Walls: SPARQL Querying of TinkerPop Graph Databases with Sparql-Gremlin. IEEE, 2020.

R. Angles, H. Thakkar, D. Tomaszuk: Mapping RDF Databases to Property Graph Databases. IEEE, 2020.

H. Jabeen, J. Weinz, J. Lehmann: AutoChef: Automated Generation of Cooking Recipes. CEC, 2020.

H. Jabeen: Big Data Outlook, Tools, and Architectures. In: Knowledge Graphs and Big Data Processing, 2020.

M. Tasnim, D. Collarana, D. Graux, M. Vidal: Context-Based Entity Matching for Big Data. In: Knowledge Graphs and Big Data Processing, 2020.

I. O. Mulang’, K. Singh, C. Prabhu, A. Nadgeri, J. Hoffart, J. Lehmann: Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models. CIKM, 2020.

J. Armitage, E. Kacupaj, G. Tahmasebzadeh, Swati, M. Maleshkova, R. Ewerth, J. Lehmann: MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities. CIKM, 2020.

M. Nayyeri, M. M. Alam, J. Lehmann, S. Vahdati: 3D Learning and Reasoning in Link Prediction Over Knowledge Graphs. IEEE, 2020.

A. Say, S. Fathalla, S. Vahdati, J. Lehmann S. Auer: Semantic Representation of Physics Research Data. IC3K, 2020.

J. Armitage, S. Thakur, R. Tripathi, J. Lehmann, M. Maleshkova: Training Multimodal Systems for Classification with Multiple Objectives. ESWC, 2020.

M. Galkin, P. Trivedi, G. Maheshwari, R. Usbeck, J. Lehmann: Message Passing for Hyper-Relational Knowledge Graphs. EMNLP, 2020.

C. Xu, M. Nayyeri, Y. Chen, J. Lehmann: Knowledge Graph Embeddings in Geometric Algebras. COLING, 2020.

C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann: TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation. COLING, 2020.

R. Nedelchev, J. Lehmann, R. Usbeck: Language Model Transformers as Evaluators for Open-domain Dialogues. COLING, 2020.

E. Iglesias, S. Jozashoori, D. Chaves-Fraga, D. Collarana, M. Vidal: SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs. CIKM, 2020.

A. Rivas, I. Grangel-González, D. Collarana, J. Lehmann, M. Vidal: Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings. DEXA, 2020.

D. Banerjee, D. Chaudhuri, M. Dubey, J. Lehmann: PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs. ISWC, 2020.

C. Ojeda, R. Sanchez, K. Cvejoski, J. Schuecker, D. Biesner, C. Bauckhage, B. Georgiev: Stochastic-Path Examples Generation for Explanations. ICPR, 2020.

C. Ojeda, B. Georgiev, K. Cvejoski, J. Schuecker, C. Bauckhage, R. Sanchez: Switching Dynamical Systems with Deep Neural Networks. ICPR, 2020.

2019

2019

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

S. Hess, W. Duivesteijn, K.Morik, P.-J. Honysz: The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. AAAI, 2019.

S. Buschjäger, T. Liebig, K. Morik: Gaussian Model Trees for Traffic Imputation. ICPRAM, 2019.

A. Saadallah, N. Piatkowski, F. Finkeldey, P. Wiederkehr, K. Morik: Learning Ensembles in the Presence of Imbalanced Classes. ICPRAM, 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. WTC, 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. ICLR, 2019.

V. Gupta, S. Giesselbach, S. Rüping, C. Bauckhage: Improving Word Embeddings Using Kernel PCA. RepL4NLP Workshop at ACL, 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.

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

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

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

S. Mücke, N. Piatkowski, K. Morik: Hardware Accelerated Learning at the Edge. DMLE Workshop at ECML, 2019.

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

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

L. Pfahler, J. Schill, K. Morik: The Search for Equations – Learning to Identify Similarities between Mathematical Expressions. ECML PKDD, 2019.

P. Welke, T. Schulz: On the Necessity of Graph Kernel Baselines. Graph Embedding and Mining Workhop at ECML PKDD, 2019.

F. Seiffarth, T. Horvath, S. Wrobel: Maximal Closed Set and Half-Space Separations in Finite Closure Systems. ECML PKDD, 2019.

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

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

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

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

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

J. HaiLong, H. Lei, L. Juanzi, T. Dong: Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks. EMNLP, 2019.

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

S. Kerner, J. Leveling , M. Otten, O. Urbann, M. Vogel, L. Weickhmann: Anwendungsfelder von künstlicher Intelligenz in Industrie 4.0 Systemen. Handbuch Industrie 4.0, 2019.

P. Tözün, H. Kotthaus: Scheduling Data-Intensive Tasks on Heterogeneous Many Cores. IEEE, 2019.

H. Kotthaus, L. Schönberger, A. Lang, J. Chen, P. Marwedel: Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently?. SCOPES, 2019.

2018

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.

S. Hess, N. Piatkowski, K. Morik: The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization. SIAM SDM, 2018.

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

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

S. Hao, X. Ma, T. Dong, A. B. Cremers, C. Chun: An Assertion Framework for Mobile Robotic Programming with Spatial Reasoning. COMPSAC, 2018.

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

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

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

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

R. Ramamurthy, C. Bauckhage, R. Sifa, S. Wrobel: Policy Learning Using SPSA. ICANN, 2018.

R. Sifa, D. Paurat, D. Trabold, C. Bauckhage: Simple Recurrent Neural Networks for Support Vector Machine Training. ICANN, 2018.

B. Wulff, J. Schücker, C. Bauckhage: SPSA for Layer-Wise Training of Deep Networks. ICANN, 2018.

Y. Cao, L. Hou, J. Li, Z. Liu, C. Li, X. Chen, T. Dong: Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision. EMNLP, 2018.

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

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

S. Giesselbach, K. Ullrich, M. Kamp, D. Paurat, T. Gärtner: Corresponding Projections for Orphan Screening. ML4H Workshop at NeurIPS, 2018.

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

B. Kirsch, S. Giesselbach, D. Knodt, 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, S. Buschjäger: Big Data Science. KI 32(1), 2018.

2017

2017

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

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

K. Ullrich, M. Kamp, T. Gärtner, M. Vogt, S. Wrobel: Co-Regularised Support Vector Regression. ECML PKDD, 2017.

L. Pfahler, K. Morik, F. Elwert, S. Tabti, V. Krech: Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization. IEEE DSAA, 2017.

R. Sifa, C. Bauckhage: Online k-Maxoids Clustering. IEEE DSAA, 2017.

C. Bauckhage, E. Brito, K. Cvejoski, C. Ojeda, R. Sifa, S. Wrobel: Ising Models for Binary Clustering via Adiabatic Quantum Computing. EMMCVPR, 2017.

H. Kotthaus, J. Richter, A. Lang, J. Thomas, B. Bischl, P. Marwedel, J. Rahnenführer, M. Lang: RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization. LION, 2017.

2016

2016

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

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

N. Piatkowski, K. Morik: Stochastic Discrete Clenshaw-Curtis Quadrature. ICML, 2016.

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

R. Sifa, S. Srikanth, A. Drachen, C. Ojeda, C. Bauckhage: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning. IEEE CIG, 2016.

J. Richter, H. Kotthaus, B. Bischl, P. Marwedel, J. Rahnenführer, M. Lang: Faster Model-Based Optimization through Resource-Aware Scheduling Strategies. LION, 2016.