Informatics 26 - Associate Professorship of Data Analytics and Machine Learning

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  • 2023

    Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness

    Franco, N., Wollschläger, T., Poggel, B., Günnemann, S. & Lorenz, J. M., 2023, Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023. Muller, H., Alexev, Y., Delgado, A. & Byrd, G. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 524-534 11 p. (Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023; vol. 1).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
  • Enabling Machine Learning in Software Architecture Frameworks

    Moin, A., Badii, A., Gunnemann, S. & Challenger, M., 2023, Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023. Institute of Electrical and Electronics Engineers Inc., p. 92-93 2 p. (Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
  • Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework

    Schwaiger, F., Matic, A., Roscher, K. & Gunnemann, S., 2023, IV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings. Institute of Electrical and Electronics Engineers Inc., (IEEE Intelligent Vehicles Symposium, Proceedings; vol. 2023-June).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry

    Wiese, J. G., Wimmer, L., Papamarkou, T., Bischl, B., Günnemann, S. & Rügamer, D., 2023, Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference, ECML PKDD 2023, Proceedings. Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E. & Bonchi, F. (eds.). Springer Science and Business Media Deutschland GmbH, p. 459-474 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14169 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    1 Scopus citations
  • 2022

    Are Defenses for Graph Neural Networks Robust?

    Mujkanovic, F., Geisler, S., Günnemann, S. & Bojchevski, A., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    9 Scopus citations
  • Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks

    Kuvshinov, A., Knobloch, D., Külzer, D., Vardanyan, E. & Günnemann, S., 30 Jun 2022, IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations. Association for the Advancement of Artificial Intelligence, p. 12552-12558 7 p. (Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022; vol. 36).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Invariance-Aware Randomized Smoothing Certificates

    Schuchardt, J. & Günnemann, S., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Scopus citations
  • MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna and ML-Quadrat

    Kirchhof, J. C., Kusmenko, E., Ritz, J., Rumpe, B., Moin, A., Badii, A., Günnemann, S. & Challenger, M., 23 Oct 2022, Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings. Association for Computing Machinery, Inc, p. 380-387 8 p. (Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Scopus citations
  • ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services

    Moin, A., Mituca, A., Challenger, M., Badii, A. & Gunnemann, S., 2022, Proceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings, ICSE-Companion 2022. IEEE Computer Society, p. 144-148 5 p. (Proceedings - International Conference on Software Engineering).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    1 Scopus citations
  • Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution

    Hetzel, L., Böhm, S., Kilbertus, N., Günnemann, S., Lotfollahi, M. & Theis, F., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    7 Scopus citations
  • Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm

    Franco, N., Wollschlager, T., Gao, N., Lorenz, J. M. & Gunnemann, S., 2022, Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022. Institute of Electrical and Electronics Engineers Inc., p. 142-153 12 p. (Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    3 Scopus citations
  • Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks

    Scholten, Y., Schuchardt, J., Geisler, S., Bojchevski, A. & Günnemann, S., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Scopus citations
  • Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning

    Schmoeller Roza, F., Rasheed, H., Roscher, K., Ning, X. & Günnemann, S., 2022, Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022. Wani, M. A., Kantardzic, M., Palade, V., Neagu, D., Yang, L. & Chan, K.-Y. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 737-742 6 p. (Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Supporting AI Engineering on the IoT Edge through Model-Driven TinyML

    Moin, A., Challenger, M., Badii, A. & Gunnemann, S., 2022, Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022. Va Leong, H., Sarvestani, S. S., Teranishi, Y., Cuzzocrea, A., Kashiwazaki, H., Towey, D., Yang, J.-J. & Shahriar, H. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 884-893 10 p. (Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    5 Scopus citations
  • Towards Model-Driven Engineering for Quantum AI

    Moin, A., Challenger, M., Badii, A. & Günnemann, S., 2022, INFORMATIK 2022 - Informatik in den Naturwissenschaften. Demmler, D., Krupka, D. & Federrath, H. (eds.). Gesellschaft fur Informatik (GI), p. 1121-1131 11 p. (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); vol. P-326).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Scopus citations
  • Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model

    Diaconu, C. A., Saha, S., Gunnemann, S. & Xiang Zhu, X., 2022, Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022. IEEE Computer Society, p. 1361-1370 10 p. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; vol. 2022-June).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    10 Scopus citations
  • 2021

    Detecting Anomalous Event Sequences with Temporal Point Processes

    Shchur, O., Türkmen, A. C., Januschowski, T., Gasthaus, J. & Günnemann, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 13419-13431 13 p. (Advances in Neural Information Processing Systems; vol. 16).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Scopus citations
  • Directional Message Passing on Molecular Graphs via Synthetic Coordinates

    Klicpera, J., Yeshwanth, C. & Günnemann, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 15421-15433 13 p. (Advances in Neural Information Processing Systems; vol. 19).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    15 Scopus citations
  • Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?

    Kopetzki, A. K., Charpentier, B., Zügner, D., Giri, S. & Günnemann, S., 2021, Proceedings of the 38th International Conference on Machine Learning, ICML 2021. ML Research Press, p. 5707-5718 12 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    18 Scopus citations
  • Gauss Shift: Density Attractor Clustering Faster Than Mean Shift

    Leibrandt, R. & Günnemann, S., 2021, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings. Hutter, F., Kersting, K., Lijffijt, J. & Valera, I. (eds.). Springer Science and Business Media Deutschland GmbH, p. 125-142 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12457 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • GemNet: Universal Directional Graph Neural Networks for Molecules

    Klicpera, J., Becker, F. & Günnemann, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 6790-6802 13 p. (Advances in Neural Information Processing Systems; vol. 9).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    141 Scopus citations
  • Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering

    Koner, R., Li, H., Hildebrandt, M., Das, D., Tresp, V. & Günnemann, S., 2021, The Semantic Web – ISWC 2021 - 20th International Semantic Web Conference, ISWC 2021, Proceedings. Hotho, A., Blomqvist, E., Dietze, S., Fokoue, A., Ding, Y., Barnaghi, P., Haller, A., Dragoni, M. & Alani, H. (eds.). Springer Science and Business Media Deutschland GmbH, p. 111-127 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12922 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    15 Scopus citations
  • Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

    Stadler, M., Charpentier, B., Geisler, S., Zügner, D. & Günnemann, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 18033-18048 16 p. (Advances in Neural Information Processing Systems; vol. 22).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    33 Scopus citations
  • In-Database Machine Learning with SQL on GPUs

    Schule, M., Lang, H., Springer, M., Kemper, A., Neumann, T. & Gunnemann, S., 6 Jul 2021, 33rd International Conference on Scientific and Statistical Database Management, SSDBM 2021, Proceedings. Zhu, Q., Zhu, X., Tu, Y., Xu, Z. & Kumar, A. (eds.). Association for Computing Machinery, p. 25-36 12 p. (ACM International Conference Proceeding Series).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    16 Scopus citations
  • Neural Flows: Efficient Alternative to Neural ODEs

    Biloš, M., Sommer, J., Rangapuram, S. S., Januschowski, T. & Günnemann, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 21325-21337 13 p. (Advances in Neural Information Processing Systems; vol. 26).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    16 Scopus citations
  • Neural Temporal Point Processes: A Review

    Shchur, O., Türkmen, A. C., Januschowski, T. & Günnemann, S., 2021, Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021. Zhou, Z.-H. (ed.). International Joint Conferences on Artificial Intelligence, p. 4585-4593 9 p. (IJCAI International Joint Conference on Artificial Intelligence).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    17 Scopus citations
  • Robustness of Graph Neural Networks at Scale

    Geisler, S., Schmidt, T., Şirin, H., Zügner, D., Bojchevski, A. & Günnemann, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 7637-7649 13 p. (Advances in Neural Information Processing Systems; vol. 10).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    47 Scopus citations
  • Scalable Normalizing Flows for Permutation Invariant Densities

    Biloš, M. & Günnemann, S., 2021, Proceedings of the 38th International Conference on Machine Learning, ICML 2021. ML Research Press, p. 957-967 11 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    4 Scopus citations
  • Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More

    Klicpera, J., Lienen, M. & Günnemann, S., 2021, Proceedings of the 38th International Conference on Machine Learning, ICML 2021. ML Research Press, p. 5616-5627 12 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Scopus citations
  • 2020

    Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection

    Roza, F. S., Henne, M., Roscher, K. & Günnemann, S., 2020, Computer Vision – ECCV 2020 Workshops, Proceedings. Bartoli, A. & Fusiello, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 3-10 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12540 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    1 Scopus citations
  • Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations

    Zügner, D. & Günnemann, S., 23 Aug 2020, KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, p. 1656-1665 10 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    35 Scopus citations
  • Efficient robustness certificates for discrete data: Sparsity-aware randomized smoothing for graphs, images and more

    Bojchevski, A., Klicpera, J. & Günnemann, S., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 980-990 11 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-2).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    40 Scopus citations
  • From things' modeling language (ThingML) to things' machine learning (ThingML2)

    Moin, A., Rössler, S., Sayih, M. & Günnemann, S., 16 Oct 2020, Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings. Association for Computing Machinery, Inc, p. 82-83 2 p. (Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    8 Scopus citations
  • Group centrality maximization for large-scale graphs

    Angriman, E., van der Grinten, A., Bojchevski, A., Zügner, D., Günnemann, S. & Meyerhenke, H., 2020, 2020 Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020. Blelloch, G. & Finocchi, I. (eds.). Society for Industrial and Applied Mathematics Publications, p. 56-69 14 p. (Proceedings of the Workshop on Algorithm Engineering and Experiments; vol. 2020-January).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    13 Scopus citations
  • Scaling Graph Neural Networks with Approximate PageRank

    Bojchevski, A., Klicpera, J., Perozzi, B., Kapoor, A., Blais, M., Rózemberczki, B., Lukasik, M. & Günnemann, S., 23 Aug 2020, KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, p. 2464-2473 10 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    151 Scopus citations
  • 2019

    Adversarial attacks on graph neural networks presentation of work originally published in the proc. Of the 24th acm sigkdd conference on knowledge discovery and data mining as well as the international conference on learning representations 2019

    Zügner, D., Akbarnejad, A. & Günnemann, S., 2019, INFORMATIK 2019: 50 Jahre Gesellschaft fur Informatik - Informatik fur Gesellschaft Konferenzbeitrage der 49. Jahrestagung der Gesellschaft fur Informatik. David, K., Geihs, K., Lange, M. & Stumme, G. (eds.). Gesellschaft fur Informatik (GI), p. 251-252 2 p. (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); vol. 294).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Scopus citations
  • Adversarial attacks on neural networks for graph data

    Zügner, D., Akbarnejad, A. & Günnemann, S., 2019, Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. Kraus, S. (ed.). International Joint Conferences on Artificial Intelligence, p. 6246-6250 5 p. (IJCAI International Joint Conference on Artificial Intelligence; vol. 2019-August).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    19 Scopus citations
  • Adversarial attacks on node embeddings via graph poisoning

    Bojchevski, A. & Günnemann, S., 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 1112-1123 12 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    105 Scopus citations
  • Certifiable robustness and robust training for graph convolutional networks

    Zügner, D. & Günnemann, S., 25 Jul 2019, KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, p. 246-256 11 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    104 Scopus citations
  • Discovering groups of signals in in-vehicle network traces for redundancy detection and functional grouping

    Mrowca, A., Moser, B. & Günnemann, S., 2019, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings. Brefeld, U., Marascu, A., Pinelli, F., Curry, E., MacNamee, B., Hurley, N., Daly, E. & Berlingerio, M. (eds.). Springer Verlag, p. 86-102 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11053 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Scopus citations
  • GhostLink: Latent network inference for influence-aware recommendation

    Mukherjee, S. & Günnemann, S., 13 May 2019, The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc, p. 1310-1320 11 p. (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    1 Scopus citations
  • In-database machine learning: Gradient descent and tensor algebra for main memory database systems

    Schüle, M., Simonis, F., Heyenbrock, T., Kemper, A., Günnemann, S. & Neumann, T., 2019, Datenbanksysteme fur Business, Technologie und Web, BTW 2019 and 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme", DBIS 2019. Grust, T., Naumann, F., Bohm, A., Lehner, W., Harder, T., Rahm, E., Heuer, A., Klettke, M. & Meyer, H. (eds.). Gesellschaft fur Informatik (GI), p. 247-266 20 p. (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); vol. P-289).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    7 Scopus citations
  • Learning temporal specifications from imperfect traces using Bayesian inference

    Mrowca, A., Nocker, M., Steinhorst, S. & Günnemann, S., 2 Jun 2019, Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc., a96. (Proceedings - Design Automation Conference).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    10 Scopus citations
  • ML2SQL: Compiling a declarative machine learning language to SQL and python

    Schüle, M. E., Bungeroth, M., Vorona, D., Kemper, A., Günnemann, S. & Neumann, T., 2019, Advances in Database Technology - EDBT 2019: 22nd International Conference on Extending Database Technology, Proceedings. Kaoudi, Z., Fundulaki, I., Reinwald, B., Galhardas, H., Binnig, C. & Herschel, M. (eds.). OpenProceedings.org, p. 562-565 4 p. (Advances in Database Technology - EDBT; vol. 2019-March).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    17 Scopus citations
  • MLearn: A declarative machine learning language for database systems

    Schüle, M. E., Bungeroth, M., Kemper, A., Günnemann, S. & Neumann, T., 30 Jun 2019, Proceedings of the 3rd Workshop on Data Management for End-To-End Machine Learning, DEEM 2019 - In conjunction with the 2019 ACM SIGMOD/PODS Conference. Association for Computing Machinery, 3329494. (Proceedings of the ACM SIGMOD International Conference on Management of Data).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    6 Scopus citations
  • Multi-source neural variational inference

    Kurle, R., Günnemann, S. & van der Smagt, P., 2019, 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019. AAAI Press, p. 4114-4121 8 p. (33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    22 Scopus citations
  • The power of SQL lambda functions

    Schüle, M. E., Vorona, D., Passing, L., Lang, H., Kemper, A., Günnemann, S. & Neumann, T., 2019, Advances in Database Technology - EDBT 2019: 22nd International Conference on Extending Database Technology, Proceedings. Galhardas, H., Binnig, C., Kaoudi, Z., Reinwald, B., Fundulaki, I. & Herschel, M. (eds.). OpenProceedings.org, p. 534-537 4 p. (Advances in Database Technology - EDBT; vol. 2019-March).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    10 Scopus citations
  • 2018

    Adversarial attacks on neural networks for graph data

    Zügner, D., Akbarnejad, A. & Günnemann, S., 19 Jul 2018, KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, p. 2847-2856 10 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
    601 Scopus citations
  • Anomaly detection in car-booking graphs

    Shchur, O., Bojchevski, A., Farghal, M., Gunnemann, S. & Saber, Y., 2 Jul 2018, Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018. Tong, H., Li, Z., Zhu, F. & Yu, J. (eds.). IEEE Computer Society, p. 604-607 4 p. 8637361. (IEEE International Conference on Data Mining Workshops, ICDMW; vol. 2018-November).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Scopus citations
  • Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure

    Bojchevski, A. & Günnemann, S., 2018, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI Press, p. 2738-2745 8 p. (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    56 Scopus citations