Research
Publications
We want to be a part of the scientific community by contributing our research results to international conferences and journals.
Introduction
Publishing enables us to collaborate and learn from the broader scientific community. Below you find a number of papers presented at international conferences and published in renowned journals sorted by date, topics and conferences.
- Publications by Date
- Publications by Conference
- Publications by Research Topic
2020 |
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2020
Becker, P., Arenz, O., & Neumann, G. (2020). Expected Information Maximization: Using the I-Projection for Mixture Density Estimation. ICLR. [Pdf]
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2020
Chubanov, S. (2020a). A polynomial algorithm for convex quadratic optimization subject to linear inequalities. Discrete Applied Mathematics, 275, 19–28. [Pdf]
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2020
Chubanov, S. (2020b). A scaling algorithm for optimizing arbitrary functions over vertices of polytopes. Mathematical Programming, para. 1. [Pdf]
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2020
De Avila Belbute-Peres, F., Economon, T., & Kolter, Z. (2020). Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction. ICML. [Pdf]
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2020
Dvijotham, K., Hayes, J., Balle, B., Kolter, Z., Qin, C., Gyorgy, A., Xiao, K., Gowal, S., & Kohli, P. (2020). A Framework for Robustness Certification of Smoothed Classifiers Using F-Divergences. ICLR. [Pdf]
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2020
Elsken, T., Staffler, B., Metzen, J. H., & Hutter, F. (2020). Meta-Learning of Neural Architectures for Few-Shot Learning. CVPR. [Pdf]
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2020
Etesami, J., & Geiger, P. (2020, April). Causal Transfer for Imitation Learning and Decision Making under Sensor-Shift. Proceedings of the AAAI Conference on Artificial Intelligence, 10118–10125. [Pdf]
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2020
Forssell, H., Kharlamov, E., & Thorstensen, E. (2020). On Equivalence and Cores for Incomplete Databases in Open and Closed Worlds. ICDT. [Pdf]
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2020
Friedrich, A., Adel, H., Tomazic, F., Benteau, R., Hingerl, J., & Marusczyk, A. (2020). The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain. ACL. [Pdf]
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2020
Friedrich, A., Adel, H., Tomazic, F., Benteau, R., Hingerl, J., & Marusczyk, A. (2020). The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain. ACL. [Pdf]
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2020
Fröhlich, L., Klenske, E., Vinogradska, J., Daniel, C., & Zeilinger, M. (2020). Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization. AISTATS. [Pdf]
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2020
Gad-Elrab, M. H., Stepanova, D., Tran, T.-K., Adel, H., & Weikum, G. (2020). ExCut: Explainable Embedding-based Clustering over Knowledge Graphs. ISWC. [Pdf]
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2020
Gargiani, M., Zanelli, A., Dinh, Q. T., Diehl, M., & Hutter, F. (2020). Transferring Optimality Across Data Distributions via Homotopy Methods. ICLR. [Pdf]
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2020
Grünewald, S., & Friedrich, A. (2020). RobertNLP at the IWPT 2020 Shared Task: Surprisingly Simple Enhanced UD Parsing for English. International Conference on Parsing Technologies and the IWPT 2020, 245. [Pdf]
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2020
Gulshad, S., & Smeulders, A. (2020). Explaining with Counter Visual Attributes and Examples. ICMR. [Pdf]
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2020
Hewing, L., Arcari, E., Froehlich, L., & Zeilinger, M. (2020). On Simulation and Trajectory Prediction with Gaussian Process Dynamics. L4DC. [Pdf]
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2020
Kersting, H., Krämer, N., Schiegg, M., Daniel, C., Schober, M., & Hennig, P. (2020). Differentiable Likelihoods for Fast Inversion of “Likelihood-Free” Dynamical Systems. ICML. [Pdf]
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2020
Kipf, T., van der Pol, E., & Welling, M. (2020). Contrastive Learning of Structured World Models. ICLR. [Pdf]
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2020
Klenske, E.D. Optimal test pooling for efficient PCR testing of SARS-CoV2. Ir J Med Sci (2020). [Pdf]
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2020
Kugele, A., Pfeil, T., Pfeiffer, M., & Chicca, E. (2020). Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks. Frontiers in Neuroscience, 14, 1–13. [Pdf]
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2020
Lange, L., Adel, H., & Stroetgen, J. (2020a). Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain. ACL. [Pdf]
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2020
Lange, L., Adel, H., & Stroetgen, J. (2020b). On the Choice of Auxiliary Languages for Improved Sequence Tagging. Workshop on Representation Learning for NLP (RepL4NLP-2020). [Pdf]
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2020
Lange, L., Iurshina, A., Adel, H., & Stroetgen, J. (2020). Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text. Workshop on Representation Learning for NLP (RepL4NLP-2020). [Pdf]
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2020
Li, J., Cheng, G., Liu, Q., Zhang, W., Kharlamov, E., Gunaratna, K., & Chen, H. (2020). Neural Entity Summarization with Joint Encoding and Weak Supervision. IJCAI. [Pdf]
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2020
Li, S., Huang, Z., Cheng, G., Kharlamov, E., & Gunaratna, K. (2020). Enriching Documents with Compact, Representative, Relevant Knowledge Graphs. IJCAI. [Pdf]
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2020
Liu, Q., Chen, Y., Cheng, G., Kharlamov, E., Li, J., & Qu, Y. (2020). Entity Summarization with User Feedback. ESWC. [Pdf]
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2020
Maini, P., Wong, E., & Kolter, Z. (2020). Adversarial Robustness Against the Union of Multiple Threat Models. ICML. [Pdf]
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2020
Nguyen, D. T., Mummadi, C. K., Ngo, T. P. N., Nguyen, T. H. P., Beggel, L., & Brox, T. (2020). SELF: Learning to Filter Noisy Labels with Self-Ensembling. ICLR. [Pdf]
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2020
Rosenfeld, E., Winston, E., Ravikumar, P., & Kolter, Z. (2020). Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. ICML. [Pdf]
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2020
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020). Human motion trajectory prediction: a survey. The International Journal of Robotics Research, 39(8), 895–935. [Pdf]
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2020
Schönfeld, E., Schiele, B., & Khoreva, A. (2020). A U-Net Based Discriminator for Generative Adversarial Networks. CVPR. [Pdf]
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2020
Schorn, C., Elsken, T., Vogel, S., Runge, A., Guntoro, A., & Ascheid, G. (2020). Automated design of error-resilient and hardware-efficient deep neural networks. Neural Computing and Applications, 12365. [Pdf]
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2020
Shi, Y., Cheng, G., & Kharlamov, E. (2020). Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings. WWW. [Pdf]
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2020
Sosnovik, I., Szmaja, M., & Smeulders, A. (2020). Scale-Equivariant Steerable Networks. ICLR. [Pdf]
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2020
Todescato, M., Carron, A., Carli, R., Pillonetto, G., & Schenato, L. (2020). Efficient spatio-temporal Gaussian regression via Kalman filtering. Automatica, 118, 109032. [Pdf]
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2020
Tran, T.-K., Gad-Elrab, M. H., Stepanova, D., Kharlamov, E., & Stroetgen, J. (2020). Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs. WWW. [Pdf]
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2020
Van der Pol, E., Kipf, T., Oliehoek, F., & Welling, M. (2020). Plannable Approximations to MDP Homomorphisms: Equivariance under actions. AAMAS. [Pdf]
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2020
Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., & Peters, J. (2020). Numerical Quadrature for Probabilistic Policy Search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(1), 164–175. [Pdf]
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2020
Volpp, M., Froehlich, L., Fischer, K., Doerr, A., Falkner, S., Hutter, F., & Daniel, C. (2020). Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization. ICLR. [Pdf]
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2020
Wang, P.-W., Stepanova, D., Domokos, C., & Kolter, J. Z. (2020). Differentiable learning of numerical rules in knowledge graphs. ICLR. [Pdf]
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2020
Wöhlke, J., Schmitt, F., & van Hoof, H. (2020). A Performance-Based Start State Curriculum Framework for Reinforcement Learning. AAMAS. [Pdf]
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2020
Wong, E., Rice, L., & Kolter, J. Z. (2020a). Fast is better than free: Revisiting adversarial training. ICLR. [Pdf]
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2020
Wong, E., Rice, L., & Kolter, Z. (2020b). Overfitting in adversarially robust deep learning. ICML. [Pdf]
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2020
Zela, A., Elsken, T., Saikia, T., Marrakchi, Y., Brox, T., & Hutter, F. (2020). Understanding and Robustifying Differentiable Architecture Search. ICLR. [Pdf]
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2020
Zela, A., Siems, J., & Hutter, F. (2020). NAS-BENCH-1SHOT1: Benchmarkting and Dissecting One-Shot Neural Architecture Search. ICLR. [Pdf]
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2020
Zimmer, C., Driess, D., Meister, M., & Nguyen-Tuong, D. (2020). Adaptive Discretization for Evaluation of Probabilistic Cost Functions. AISTATS. [Pdf]
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2019 |
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2019
Agrawal, A., Amos, B., Barratt, S., Boyd, S., Diamond, S., & Kolter, J. Z. (2019). Differentiable Convex Optimization Layers. NeurIPS. [Pdf]
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2019
Akrour, R., Pajarinen, J., & Neumann, G. (2019). Projections for Approximate Policy Iteration Algorithms. ICML. [Pdf]
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2019 |
2019
Arvanitis, G., Hauberg, S., Henning, P., & Schober, M. (2019). Fast and Robust Shortest Paths on Manifolds Learned from Data. AISTATS. [Pdf]
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2019
Bai, S., Koltun, V., & Kolter, J. Z. (2019). Deep Equilibrium Models. NeurIPS. [Pdf]
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2019
Becker, P., Pandya, H., Gebhardt, G., Zhao, C., Taylor, J., & Neumann, G. (2019). Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces. ICML. [Pdf]
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2019
Beggel, L., Pfeiffer, M., & Bischl, B. (2019). Robust Anomaly Detection in Images using Adversarial Autoencoders. ECML. [Pdf]
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2019
Berg, S., Kutra, D., Kroeger, T., Straehle, C. N., Kausler, B. X., Haubold, C., Schiegg, M., Ales, J., Beier, T., Rudy, M., Eren, K., Cervantes, J. I., Xu, B., Beuttenmueller, F., Wolny, A., Zhang, C., Koethe, U., Hamprecht, F. A., & Kreshuk, A. (2019). ilastik: interactive machine learning for (bio)image analysis. Nature Methods, 16(12), 1226–1232. [Pdf]
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2019
Bhattacharyya, A., Hanselmann, M., Fritz, M., Schiele, B., & Straehle, C.-N. (2019, December). Conditional Flow Variational Autoencoders for Structured Sequence Prediction [Workshop]. NeurIPS, Vancouver, Canada.
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2019
Blaiotta, C. (2019). Learning Generative Socially Aware Models of Pedestrian Motion. IEEE Robotics and Automation Letters, 4(4), 3433–3440. [Pdf]
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2019
Chen, J., Wang, X., Cheng, G., Kharlamow, E., & Qu, Y. (2019). Towards More Usable Dataset Search: From Query Characterization to Snippet Generation. CIKM. [Pdf]
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2019
Cohen, J., Rosenfeld, E., & Kolter, Z. (2019). Certified Adversarial Robustness via Randomized Smoothing. ICML. [Pdf]
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2019 |
2019
Dikeoulias, I., Strötgen, J., & Razniewski, S. (2019). Epitaph or Breaking News? Analyzing and Predicting the Stability of Knowledge Base Properties. TempWeb. [Pdf]
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2019
Doellinger, J., Prabhakaran, V. S., Fu, L., & Spies, M. (2019). Environment-Aware Multi-Target Tracking of Pedestrians. IEEE Robotics and Automation Letters, 4(2), 1831–1837. [Pdf]
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2019
Doerr, A., Volpp, M., Toussaint, M., Trimpe, S., & Daniel, C. (2019). Trajectory-Based Off-Policy Deep Reinforcement Learning. ICML. [Pdf]
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2019
Elsken, T., Metzen, J. H., & Hutter, F. (2019a). Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution. ICLR. [Pdf]
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2019
Elsken, T., Metzen, J. H., & Hutter, F. (2019b). Neural Architecture Search: A Survey. Journal of Machine Learning Research 20 (2, 1–21. [Pdf]
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2019 |
2019
Fischer, F., Xiao, H., Kao, C., Stachelscheid, Y., Johnson, B., Razar, D., Furley, P., Buckley, N., Boettinger, K., Muntean, P., & Grossklags, J. (2019). Stack Overflow Considered Helpful! Deep Learning Security Nudges Towards Stronger Cryptography. Usenix Security Symposium. [Pdf]
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2019
Friedrich, A., Tran, T.-K., Milchevski, D., Tomazic, F., Marusczyk, A., Adel, H., Stepanova, D., Stroetgen, J., Hildebrand, F., & Kharlamov, E. (2019). Towards the Bosch Materials Science Knowledge Base. ISWC (Industry Track). [Pdf]
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2019
Gad-Elrab, M., Stepanova, D., Urbani, J., & Weikum, G. (2019a). ExFact: Explaining Facts over Knowledge Graphs and Text. WSDM. [Pdf]
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2019
Gad-Elrab, M., Stepanova, D., Urbani, J., & Weikum, G. (2019b). Tracy: Tracing Facts over Knowledge Graphs and Text. WWW. [Pdf]
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2019
Garcia, V., Akata, Z., & Welling, M. (2019). GRIN: Graphical Recurrent Inference Networks. NeurIPS. [Pdf]
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2019
Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., & Schölkopf, B. (2019). Coordinating users of shared facilities via data-driven predictive assistants and game theory. UAI. [Pdf]
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2019
Guo, M., & Bürger, M. (2019). Predictive Safety Network for Resource-constrained Multi-agent Systems. CoRL. [Pdf]
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2019
Gupta, D., Berberich, K., Strötgen, J., & Zeinalipour-Yazti, D. (2019). Generating Semantic Aspects for Queries. ESWC. [Pdf]
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2019
Haussmann, M., Hamprecht, F. A., & Kandemir, M. (2019a). Deep Active Learning with Adaptive Acquisition. IJCAI. [Pdf]
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2019
Haussmann, M., Hamprecht, F. A., & Kandemir, M. (2019b). Sampling-Free Variational Inference of Bayesian Neural Nets with Variance Backpropagation. UAI. [Pdf]
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2019
Hoogeboom, E., Peters, J., van den Berg, R., & Welling, M. (2019). Integer Discrete Flows and Lossless Compression. NeurIPS. [Pdf]
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2019
Hoogeboom, E., Van den Berg, R., & Welling, M. (2019). Emerging Convolutions for Generative Normalizing Flows. ICML. [Pdf]
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2019
Hoyer, L., Kesper, P., Khoreva, A., & Fischer, V. (2019). Short-Term Prediction and Multi-Camera Fusion on Semantic Grids. ICCV. [Pdf]
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2019
Hoyer, L., Munoz, M., Katiyar, P., Khoreva, A., & Fischer, V. (2019). Grid Saliency for Context Explanations of Semantic Segmentation. NeurIPS. [Pdf]
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2019 |
2019 |
2019
Huang, Z., Li, S., Cheng, G., Kharlamov, E., & Qu, Y. (2019). Making Sense of News via Relationship Subgraphs. CIKM. [Pdf]
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2019
Jaquier, N., Rozo, L., Calinon, S., & Bürger, M. (2019). Bayesian Optimization Meets Riemannian Manifolds in Robot Learning. CoRL. [Pdf]
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2019
John, D., Heuveline, V., & ERROR: No link has been specified! (2019). GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver. ICML. [Pdf]
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2019 |
2019
Kharlamov, E., Kotidis, Y., Mailis, T., Neuenstadt, C., Nikolaou, C., Ozcep, O., Christoforos Svingos, C., Zheleznyakov, D., Ioannidis, Y., Lamparter, S., & Moller, R. (2019). An Ontology-Mediated Analytics-Aware Approach to Support Monitoring and Diagnostics of Static and Streaming Data. SSRN Electronic Journal, 1–34. [Pdf]
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2019
Klungre, V. N., Soylu, A., Jimenez-Ruiz, E., Kharlamov, E., & Giese, M. (2019). Query Extension Suggestions for Visual Query Systems Through Ontology Projection and Indexing. New Generation Computing, 37(4), 361–392. [Pdf]
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2019
Koch, M., Spies, M., & Bürger, M. (2019). Trust Regions for Safe Sampling-Based Model Predictive Control. ICRA. [Pdf]
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2019
Köhler, J., Autenrieth, M., & Beluch, W. (2019). Uncertainty Based Detection and Relabeling of Noisy Image Labels. CVPR. [Pdf]
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2019 |
2019 |
2019 |
2019
Lange, L., Alonso, O., & Strötgen, J. (2019). The Power of Temporal Features for Classifying News Articles. TempWeb. [Pdf]
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2019
Lange, L., Hedderich, M. A., & Klakow, D. (2019). Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels. EMNLP. [Pdf]
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2019
Li, B., Schmidt, F. R., & Kolter, Z. (2019). Adversarial camera stickers: A physical camera-based attack on deep learning systems. ICML. [Pdf]
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2019
Li, J., Qu, S., Li, X., Szurley, J., Kolter, J. Z., & Metze, F. (2019). Adversarial Music: Real world Audio Adversary against Wake-word Detection System. NeurIPS. [Pdf]
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2019
Look, A., & Kandemir, M. (2019, December). Differential Bayesian Neural Nets [Workshop: Poster]. NeurIPS, Vancouver, Canada. [Pdf]
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2019
Louizos, C., Shi, X., & Welling, M. (2019). The Functional Neural Process. NeurIPS. [Pdf]
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2019
Mailis, T., Kotidis, Y., Nikolopoulos, V., Kharlamov, E., Horrocks, I., & Ioannidis, Y. (2019a). An Efficient Index for RDF Query Containment. SIGMOD. [Pdf]
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2019
Mailis, T., Kotidis, Y., Nikolopoulos, V., Kharlamov, E., Horrocks, I., & Ioannidis, Y. (2019b). Mv-index: An Efficient Index for Graph-Query Containment. ISWC. [Pdf]
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2019
Manek, G., & Kolter, J. Z. (2019). Learning Stable Deep Dynamics Models. NeurIPS. [Pdf]
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2019 |
2019
Mehdi, A., Kharlamov, E., Stepanova, D., Loesch, F., & Gonzales, I. G. (2019). Towards Semantic Integration of Bosch Manufacturing Data. ISCW (Industry Track). [Pdf]
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2019
Mettes, P., van der Pol, E., & Snoek, C. (2019). Hyperspherical Prototype Networks. NeurIPS. [Pdf]
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2019
Mummadi, C. K., Brox, T., & Metzen, J. H. (2019). Defending against universal perturbations with shared adversarial training. ICCV. [Pdf]
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2019
Nagarajan, V., & Kolter, J. Z. (2019). Uniform convergence may be unable to explain generalization in deep learning. NeurIPS. [Pdf]
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2019
Nguyen, D. T., Dax, M., Mummadi, C. K., Ngo, N., Hoai, T., Nguyen, P., Lou, Z., & Brox, T. (2019). DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision. NeurIPS. [Pdf]
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2019
Patel, K., Rambach, K., Visentin, T., Rusev, D., Pfeiffer, M., & Yang, B. (2019). Deep Learning-based Object Classification on Automotive Radar Spectra. IEEE Radar. [Pdf]
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2019 |
2019
Savkovic, O., Kharlamov, E., & Lamparter, S. (2019). Validation of SHACL Constraints over KGs with OWL 2 Ontologies via Rewriting. ESWC. [Pdf]
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2019
Schönfeld, E., Ebrahimi, S., Sinha, S., Darrall, T., & Akata, Z. (2019). Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders. CVPR. [Pdf]
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2019
Schwab, M., Jäschke, R., Fischer, F., & Stroetgen, J. (2019). “A Buster Keaton of Linguistics”: First Automated Approaches for the Extraction of Vossian Antonomasia. EMNLP. [Pdf]
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2019
Schwenkel, L., Guo, M., & Bürger, M. (2019). Optimizing Sequences of Probabilistic Manipulation Skills Learned from Demonstration. CoRL. [Pdf]
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2019 |
2019
Spies, M., Todescato, M., Becker, H., Kesper, P., Waniek, N., & Guo, M. (2019). Bounded Suboptimal Search with Learned Heuristics for Multi-Agent Systems. AAAI. [Pdf]
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2019
Trouleau, W., Etesami, J., Grossglauser, M., Kiyavash, M., & Thiran, P. (2019). Learning Hawkes Processes Under Synchronization Noise. ICML. [Pdf]
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2019
Wagner, J., Köhler, J. M., Gindele, T., Hetzel, L., Wiedemer, J. T., & Behnke, S. (2019). Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks. CVPR. [Pdf]
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2019
Wang, P.-W., Donti, P., Wilder, B., & Kolter, Z. (2019). SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver. ICML. [Pdf]
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2019
Wang, X., Chen, J., Li, S., Cheng, G., Pan, J. Z., Kharlamov, E., & Qu, Y. (2019). A Framework for Evaluating Snippet Generation for Dataset Search. ISWC. [Pdf]
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2019
Wang, X., Cheng, G., & Kharlamov, E. (2019). Towards Multi-Facet Snippets for Dataset Search. PROFILES. [Pdf]
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2019
Waniek, N. (2020). Transition Scale-Spaces: A Computational Theory for the Discretized Entorhinal Cortex. Neural Computation, 32(2), 330–394. [Pdf]
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2019
Wong, E., Schmidt, F. R., & Kolter, Z. (2019). Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. ICML. [Pdf]
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2019
Zafar, M. B., Valera, I., Gomez-Rodriguez, M., & Gummadi, K. P. (2019). Fairness Constraints: A Flexible Approach for Fair Classification. Journal of Machine Learning Research 20, 1–42. [Pdf]
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2019 |
2019
Zheleznyakov, D., Kharlamov, E., Nutt, W., & Calvanese, D. (2019). On Expansion and Contraction of DL-Lite Knowledge Bases. SSRN Electronic Journal, 1–23. [Pdf]
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2018 |
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2018
Achterhold, J., Köhler, J. M., Schmeink, A., & Genewein, T. (2018). Variational Network Quantization. ICLR. [Pdf]
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2018
Agarwal, P., Strötgen, J., del Corro, L., Hoffart, J., & Weikum, G. (2018). diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora. ACL. [Pdf]
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2018
Beggel, L., Kausler, B. X., Schiegg, M., Pfeiffer, M., & Bischl, B. (2018). Time series anomaly detection based on shapelet learning. Computational Statistics, 34(3), 945–976. [Pdf]
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2018
Conner D.C. et al. (2018) Collaborative Autonomy Between High-Level Behaviors and Human Operators for Control of Complex Tasks with Different Humanoid Robots. In: Spenko M., Buerger S., Iagnemma K. (eds) The DARPA Robotics Challenge Finals: Humanoid Robots To The Rescue. Springer Tracts in Advanced Robotics, vol 121. Springer, Cham. [Pdf]
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2018
Domokos, C., Schmidt, F. R., & Cremers, D. (2018). MRF Optimization with Separable Convex Prior on Partially Ordered Labels. ECCV. [Pdf]
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2018
Domokos, C., Schmidt, F. R., & Cremers, D. (2018). MRF Optimization with Separable Convex Prior on Partially Ordered Labels. ECCV. [Pdf]
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2018
Estellers, V., Schmidt, F. R., & Cremers, D. (2018). Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis. 3DV. [Pdf]
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2018
Etesami, J., Habibnia, A., & Kiyavash, N. (2018). Econometric Modeling of Systemic Risk: A Time Series Approach. KDD. [Pdf]
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2018
Fischer, V., Pfeil, T., & Köhler, J. (2018). The streaming rollout of deep networks - towards fully model-parallel execution. NIPS. [Pdf]
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2018
Gutzeit, L., Fabisch, A., Otto, M., Metzen, J. H., Hansen, J., Kirchner, F., & Kirchner, E. A. (2018). The BesMan Learning Platform for Automated Robot Skill Learning. Frontiers in Robotics and AI, 5, para. 1. [Pdf]
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2018
Pfeiffer, M., & Pfeil, T. (2018). Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience, 12, para. 1. [Pdf]
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2018
Salehkaleybar, S., Etesami, J., Kiyavash, N., & Zhang, K. (2018). Learning Vector Autoregressive Models with Latent Processes. AAAI. [Pdf]
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2018
Tayeb, Z., Waniek, N., Fedjaev, J., Ghaboosi, N., Rychly, L., Widderich, C., Richter, C., Braun, J., Saveriano, M., Cheng, G., & Conradt, J. (2018). Gumpy: a Python toolbox suitable for hybrid brain–computer interfaces. Journal of Neural Engineering, 15(6), 65003. [Pdf]
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2018
Waniek, N. (2018). Hexagonal Grid Fields Optimally Encode Transitions in Spatiotemporal Sequences. Neural Computation, 30(10), 2691–2725. [Pdf]
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2018
Wong, E., Schmidt, F. R., Metzen, J. H., & Kolter, Z. (2018). Scaling provable adversarial defenses. NIPS. [Pdf]
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2017 |
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2017
Doerr, A., Daniel, C., Nguyen-Tuong, D., Marco, A., Schaal, S., Toussaint, M., & Trimpe, S. (2017). Optimizing Long-term Predictions for Model-based Policy Search. CoRL. [Pdf]
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2017
Doerr, A., Nguyen-Tuong, D., Marco, A., Schaal, S., & Trimpe, S. (2017). Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers. ICRA. [Pdf]
|
2017
Fischer, V., Kumar, M. C., Metzen, J. H., & Brox, T. (2017). Adversarial examples for semantic image segmentation. ICLR. [Pdf]
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2017
Gondal, W. M., Köhler, J. M., Grzeszick, R., Fink, G. A., & Hirsch, M. (2017). Weakly-Supervised Localization of Diabetic Retinopathy Lesions in Retinal Fundus Images. ICIP. [Pdf]
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2017
Heit, J., Liu, J., & Shah, M. (2017). An Architecture for the Deployment of Statistical Models for the Big Data Era. IEEE. [Pdf]
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2017
Meister, M., & Steinwart, I. (2017). Optimal Learning Rates for Localized SVMs. JMLR. [Pdf]
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2017
Metzen, J. H., Genewein, T., Fischer, V., & Bischoff, B. (2017). On Detecting Adversarial Perturbations. ICLR. [Pdf]
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2017
Metzen, J. H., Kumar, M. C., Brox, T., & Fischer, V. (2017). Universal Adversarial Perturbations Against Semantic Image Segmentation. ICCV. [Pdf]
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2017
Schillinger, M., Hartmann, B., Skalecki, P., Meister, M., Nguyen-Tuong, D., & Nelles, O. (2017). Safe Active Learning and Bayesian Optimization for Tuning a PI-Controller. IFAC. [Pdf]
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2017
Schillinger, P., Bürger, M., & Dimarogonas, D. V. (2017). Multi-objective search for optimal multi-robot planning with finite LTL specifications and resource constraints. ICRA. [Pdf]
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2017
Wagner, J., Fischer, V., Herman, M., & Behnke, S. (2017). Learning Semantic Prediction using Pretrained Deep Feedforward Networks. ESANN. [Pdf]
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2017
Zhang, S., Bahrampour, S., & Ramakrishnan, N. (2017). Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. IEEE Xplore. [Pdf]
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2016 |
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2016
Daniel, C., van Hoof, H., Peters, J., & Neumann, G. (2016). Probabilistic inference for determining options in reinforcement learning. Machine Learning, 104(2–3), 337–357. [Pdf]
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2016
Dhar, S., Naveen, N., Cherkassky, V., & Shah, M. (2016). Universum Learning for Multiclass SVM. SVM. [Pdf]
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2016
Hartmann, B., Kloppenburg, E., Heuser, P., & Diener, R. (2016). Online-Methods for Engine Test Bed Measurements Considering Engine Limits. ISSAM. [Pdf]
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2016
Heit, J., Liu, J., & Shah, M. (2016a). An Architecture for the Deployment of Statistical Models for the Big Data Era. IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7840745&tag=1
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2016
Heit, J., Liu, J., & Shah, M. (2016b). An architecture for the deployment of statistical models for the big data era. IEEE. [Pdf]
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2016
Herman, M., Gindele, T., Wagner, J., Schmitt, F., & Burgard, W. (2016a). Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics. AISTATS. [Pdf]
|
2016
Herman, M., Gindele, T., Wagner, J., Schmitt, F., & Burgard, W. (2016b). Simultaneous Estimation of Rewards and Dynamics from Noisy Expert Demonstrations. ESANN. [Pdf]
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2016
Koerts, F., Bürger, M., van der Schaft, A., & De Persis, C. (2016). Stability Analysis of Networked Systems Containing Damped and Undamped Nodes. ACC. [Pdf]
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2016
Metzen, J. H. (2016). Minimum Regret Search for Single- and Multi-Task Optimization. ICML. [Pdf]
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2016
Peters, J., Lee, D. D., Kober, J., Nguyen-Tuong, D., Bagnell, J. A., & Schaal, S. (2016). Robot Learning (Handbook ed.). Springer, Cham. [Pdf]
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2016
Schiegg, M., Diego, F., & Hamprecht, F. A. (2016). Learning Diverse Models: The Coulomb Structured Support Vector Machine. ECCV. [Pdf]
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2016
Schillinger, M., Ortelt, B., Hartman, B., Schreiter, J., Meister, M., & Nelles, O. (2016). Safe Active Learning of High Pressure Fuel Supply Systems. EUROSIM. [Pdf]
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2016
Schillinger, P., Bürger, M., & Dimarogonas, D. V. (2016). Decomposition of Finite LTL Specifications for Efficient Multi-Agent Planning. DARS. [Pdf]
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2016
Schmitt, F., Bieg, H.-J., Manstetten, D., Herman, M., & Stiefelhagen, R. (2016). Predicting Lane Keeping Behavior of Visually Distracted Drivers Using Inverse Suboptimal Control. IV. [Pdf]
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2016
Schreiter, J., Nguyen-Tuong, D., & Toussaint, M. (2016). Efficient Sparsification for Gaussian Process Regression. Neuro Comp. [Pdf]
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2016
Vinogradska, J., Bischoff, B., Nguyen-Tuong, D., Schmidt, H., Romer, A., & Peters, J. (2016). Stability of Controllers for Gaussian Process Forward Models. ICML. [Pdf]
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2016
Wagner, J., Fischer, V., Herman, M., & Behnke, S. (2016). Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks. ESANN. [Pdf]
|
2015 |
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2015
Dhar, S., Ramakrishnan, C. Y. N., & Shah, M. (2015). ADMM based Scalable Machine Learning on Spark. ADMM. [Pdf]
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2015
Herman, M., Fischer, V., Gindele, T., & Burgard, W. (2015). Inverse Reinforcement Learning of Behavioral Models for Online-Adapting Navigation Strategies. ICRA. [Pdf]
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2015
Schreiter, J., Englert, P., Nguyen-Tuong, D., & Toussaint, M. (2015). Sparse Gaussian Process Regression for Compliant, Real-time Robot Control. ICRA. [Pdf]
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2015
Schreiter, J., Nguyen-Tuong, D., Eberts, M., Bischoff, B., Markert, H., & Toussaint, M. (2015). Safe Exploration for Active Learning with Gaussian Processes. ECML. [Pdf]
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2014 |
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2014
Bischoff, B., Nguyen-Tuong, D., Koller, T., Markert, H., & Knoll, A. (2014). Learning Throttle Valve Control Using Policy Search. ECML. [Pdf]
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2014
Bischoff, B., ERROR: No link has been specified!, van Hoof, H., McHutchon, A., Rasmussen, C. E., Knoll, A., Peters, J., & Deisenroth, M. P. (2014). Policy Search for Learning Robot Control Using Sparse Data. ICRA. [Pdf]
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2014
Tietze, N., Konigorski, U., Fleck, C., & Nguyen-Tuong, D. (2014). Model-based Calibration of Engine Controller Using Automated Transient Design of Experiment. ISSAM. [Pdf]
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2013 |
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2013
Bischoff, B., Markert, H., Knoll, A., & Nguyen-Tuong, D. (2013). Solving the 15-Puzzle Game Using Local Value-Iteration. SCAI. [Pdf]
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2013
Bischoff, B., Nguyen-Tuong, D., Markert, H., & Knoll, A. (2013). Learning Control Under Uncertainty: A Probabilistic Value-Iteration Approach. ESANN. [Pdf]
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2013
Bischoff, B., Nguyen-Tuong, D., Lee, I.-H., Streichert, F., & Knoll, A. (2013). Hierarchical Reinforcement Learning for Robot Navigation. ESANN. [Pdf]
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2012 |
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2012
Bischoff, B., Nguyen-Tuong, D., Streichert, F., Ewert, M., & Knoll, A. (2012). Fusing Vision and Odometry for Accurate Indoor Robot Localization. ICARCV. [Pdf]
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2012
ERROR: No link has been specified!, & Peters, J. (2012). Online Kernel-Based Learning for Task-Space Tracking Robot Control. TransNN. [Pdf]
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ICML |
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ICML
De Avila Belbute-Peres, F., Economon, T., & Kolter, Z. (2020). Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction. ICML. [Pdf]
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ICML
Kersting, H., Krämer, N., Schiegg, M., Daniel, C., Schober, M., & Hennig, P. (2020). Differentiable Likelihoods for Fast Inversion of “Likelihood-Free” Dynamical Systems. ICML. [Pdf]
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ICML
Maini, P., Wong, E., & Kolter, Z. (2020). Adversarial Robustness Against the Union of Multiple Threat Models. ICML. [Pdf]
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ICML
Rosenfeld, E., Winston, E., Ravikumar, P., & Kolter, Z. (2020). Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. ICML. [Pdf]
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ICML
Wong, E., Rice, L., & Kolter, Z. (2020b). Overfitting in adversarially robust deep learning. ICML. [Pdf]
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ICML
Akrour, R., Pajarinen, J., & Neumann, G. (2019). Projections for Approximate Policy Iteration Algorithms. ICML. [Pdf]
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ICML
Becker, P., Pandya, H., Gebhardt, G., Zhao, C., Taylor, J., & Neumann, G. (2019). Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces. ICML.
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ICML
Cohen, J., Rosenfeld, E., & Kolter, Z. (2019). Certified Adversarial Robustness via Randomized Smoothing. ICML. [Pdf]
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ICML
Dörr, A., Volpp, M., Toussaint, M., Trimpe, S., & Daniel, C. (2019). Trajectory-Based Off-Policy Deep Reinforcement Learning. ICML. [Pdf]
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ICML
Hoogeboom, E., Van den Berg, R., & Welling, M. (2019). Emerging Convolutions for Generative Normalizing Flows. ICML. [Pdf]
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ICML
John, D., Heuveline, V., & Schober, M. (2019). GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver. ICML. [Pdf]
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ICML
Li, B., Schmidt, F. R., & Kolter, Z. (2019). Adversarial camera stickers: A physical camera-based attack on deep learning systems. ICML. [Pdf]
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ICML
Trouleau, W., Etesami, J., Grossglauser, M., Kiyavash, M., & Thiran, P. (2019). Learning Hawkes Processes Under Synchronization Noise. ICML. [Pdf]
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ICML
Wang, P.-W., Donti, P., Wilder, B., & Kolter, Z. (2019). SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver. ICML. [Pdf]
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ICML
Wong, E., Schmidt, F. R., & Kolter, Z. (2019). Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. ICML. [Pdf]
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NeurIPS |
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NeurIPS
Agrawal, A., Amos, B., Barratt, S., Boyd, S., Diamond, S., & Kolter, J. Z. (2019). Differentiable Convex Optimization Layers. NeurIPS. [Pdf]
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NeurIPS
Bai, S., Koltun, V., & Kolter, J. Z. (2019). Deep Equilibrium Models. NeurIPS. [Pdf]
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NeurIPS
Bhattacharyya, A., Hanselmann, M., Fritz, M., Schiele, B., & Straehle, C.-N. (2019, December). Conditional Flow Variational Autoencoders for Structured Sequence Prediction [Workshop]. NeurIPS, Vancouver, Canada.
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NeurIPS
Garcia, V., Akata, Z., & Welling, M. (2019). GRIN: Graphical Recurrent Inference Networks. NeurIPS. [Pdf]
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NeurIPS
Hoogeboom, E., Peters, J., van den Berg, R., & Welling, M. (2019). Integer Discrete Flows and Lossless Compression. NeurIPS. [Pdf]
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NeurIPS
Hoyer, L., Munoz, M., Katiyar, P., Khoreva, A., & Fischer, V. (2019). Grid Saliency for Context Explanations of Semantic Segmentation. NeurIPS. [Pdf]
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NeurIPS
Li, J., Qu, S., Li, X., Szurley, J., Kolter, J. Z., & Metze, F. (2019). Adversarial Music: Real world Audio Adversary against Wake-word Detection System. NeurIPS. [Pdf]
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NeurIPS
Look, A., & Kandemir, M. (2019, December). Differential Bayesian Neural Nets [Workshop: Poster]. NeurIPS, Vancouver, Canada. [Pdf]
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NeurIPS
Louizos, C., Shi, X., & Welling, M. (2019). The Functional Neural Process. NeurIPS. [Pdf]
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NeurIPS
Manek, G., & Kolter, J. Z. (2019). Learning Stable Deep Dynamics Models. NeurIPS. [Pdf]
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NeurIPS
Mettes, P., van der Pol, E., & Snoek, C. (2019). Hyperspherical Prototype Networks. NeurIPS. [Pdf]
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NeurIPS
Nagarajan, V., & Kolter, J. Z. (2019). Uniform convergence may be unable to explain generalization in deep learning. NeurIPS. [Pdf]
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NeurIPS
Nguyen, D. T., Dax, M., Mummadi, C. K., Ngo, N., Hoai, T., Nguyen, P., Lou, Z., & Brox, T. (2019). DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision. NeurIPS. [Pdf]
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NeurIPS
Zhang, D., & Khoreva, A. (2019). Progressive Augmentation of GANs. NeurIPS. [Pdf]
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ICLR |
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ICLR
Becker, P., Arenz, O., & Neumann, G. (2020). Expected Information Maximization: Using the I-Projection for Mixture Density Estimation. ICLR. [Pdf]
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ICLR
Dvijotham, K., Hayes, J., Balle, B., Kolter, Z., Qin, C., Gyorgy, A., Xiao, K., Gowal, S., & Kohli, P. (2020). A Framework for Robustness Certification of Smoothed Classifiers Using F-Divergences. ICLR. [Pdf]
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ICLR
Gargiani, M., Zanelli, A., Dinh, Q. T., Diehl, M., & Hutter, F. (2020). Transferring Optimality Across Data Distributions via Homotopy Methods. ICLR. [Pdf]
|
ICLR
Kipf, T., van der Pol, E., & Welling, M. (2020). Contrastive Learning of Structured World Models. ICLR. [Pdf]
|
ICLR
Nguyen, D. T., Mummadi, C. K., Ngo, T. P. N., Nguyen, T. H. P., Beggel, L., & Brox, T. (2020). SELF: Learning to Filter Noisy Labels with Self-Ensembling. ICLR. [Pdf]
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ICLR
Sosnovik, I., Szmaja, M., & Smeulders, A. (2020). Scale-Equivariant Steerable Networks. ICLR. [Pdf]
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ICLR
Volpp, M., Froehlich, L., Fischer, K., Doerr, A., Falkner, S., Hutter, F., & Daniel, C. (2020). Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization. ICLR. [Pdf]
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ICLR
Wang, P.-W., Stepanova, D., Domokos, C., & Kolter, J. Z. (2020). Differentiable learning of numerical rules in knowledge graphs. ICLR. [Pdf]
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ICLR
Wong, E., Rice, L., & Kolter, J. Z. (2020a). Fast is better than free: Revisiting adversarial training. ICLR. [Pdf]
|
ICLR
Zela, A., Elsken, T., Saikia, T., Marrakchi, Y., Brox, T., & Hutter, F. (2020). Understanding and Robustifying Differentiable Architecture Search. ICLR. [Pdf]
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ICLR
Zela, A., Siems, J., & Hutter, F. (2020). NAS-BENCH-1SHOT1: Benchmarkting and Dissecting One-Shot Neural Architecture Search. ICLR. [Pdf]
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ICLR
Elsken, T., Metzen, J. H., & Hutter, F. (2019a). Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution. ICLR. [Pdf]
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ICLR
Achterhold, J., Köhler, J. M., Schmeink, A., & Genewein, T. (2018). Variational Network Quantization. ICLR. [Pdf]
|
ICLR
Fischer, V., Kumar, M. C., Metzen, J. H., & Brox, T. (2017). Adversarial examples for semantic image segmentation. ICLR. [Pdf]
|
ICLR
Metzen, J. H., Genewein, T., Fischer, V., & Bischoff, B. (2017). On Detecting Adversarial Perturbations. ICLR. [Pdf]
|
Deep Learning |
---|
Deep Learning
Wong, E., Rice, L., & Kolter, Z. (2020b). Overfitting in adversarially robust deep learning. ICML. [Pdf]
|
Deep Learning
Fischer, F., Xiao, H., Kao, C., Stachelscheid, Y., Johnson, B., Razar, D., Furley, P., Buckley, N., Boettinger, K., Muntean, P., & Grossklags, J. (2019). Stack Overflow Considered Helpful! Deep Learning Security Nudges Towards Stronger Cryptography. Usenix Security Symposium. [Pdf]
|
Deep Learning
Li, B., Schmidt, F. R., & Kolter, Z. (2019). Adversarial camera stickers: A physical camera-based attack on deep learning systems. ICML. [Pdf]
|
Deep Learning
Nagarajan, V., & Kolter, J. Z. (2019). Uniform convergence may be unable to explain generalization in deep learning. NeurIPS. [Pdf]
|
Deep Learning
Patel, K., Rambach, K., Visentin, T., Rusev, D., Pfeiffer, M., & Yang, B. (2019). Deep Learning-based Object Classification on Automotive Radar Spectra. IEEE Radar. [Pdf]
|
Deep Learning
Wang, P.-W., Donti, P., Wilder, B., & Kolter, Z. (2019). SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver. ICML. [Pdf]
|
Deep Learning
Pfeiffer, M., & Pfeil, T. (2018). Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience, 12, para. 1. [Pdf]
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Deep Learning
Zhang, S., Bahrampour, S., & Ramakrishnan, N. (2017). Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. IEEE Xplore. [Pdf]
|
Hybrid Models |
---|
Hybrid Models
Tayeb, Z., Waniek, N., Fedjaev, J., Ghaboosi, N., Rychly, L., Widderich, C., Richter, C., Braun, J., Saveriano, M., Cheng, G., & Conradt, J. (2018). Gumpy: a Python toolbox suitable for hybrid brain–computer interfaces. Journal of Neural Engineering, 15(6), 65003. [Pdf]
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Probabilistic Modeling |
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Probabilistic Modeling
Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., & Peters, J. (2020). Numerical Quadrature for Probabilistic Policy Search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(1), 164–175. [Pdf]
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Probabilistic Modeling
Zimmer, C., Driess, D., Meister, M., & Nguyen-Tuong, D. (2020). Adaptive Discretization for Evaluation of Probabilistic Cost Functions. AISTATS. [Pdf]
|
Probabilistic Modeling
Schwenkel, L., Guo, M., & Bürger, M. (2019). Optimizing Sequences of Probabilistic Manipulation Skills Learned from Demonstration. CoRL. [Pdf]
|
Probabilistic Modeling
Daniel, C., van Hoof, H., Peters, J., & Neumann, G. (2016). Probabilistic inference for determining options in reinforcement learning. Machine Learning, 104(2–3), 337–357. [Pdf]
|
Probabilistic Modeling
Bischoff, B., Nguyen-Tuong, D., Markert, H., & Knoll, A. (2013). Learning Control Under Uncertainty: A Probabilistic Value-Iteration Approach. ESANN. [Pdf]
|
Reinforcement Learning |
---|
Reinforcement Learning
Wöhlke, J., Schmitt, F., & van Hoof, H. (2020). A Performance-Based Start State Curriculum Framework for Reinforcement Learning. AAMAS. http://www.ifaamas.org/Proceedings/aamas2020/pdfs/p1503.pdf
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Reinforcement Learning
Dörr, A., Volpp, M., Toussaint, M., Trimpe, S., & Daniel, C. (2019). Trajectory-Based Off-Policy Deep Reinforcement Learning. ICML. https://arxiv.org/pdf/1905.05710
|
Reinforcement Learning
Esteban, D., Rozo, L., & Caldwell, D. G. (2019). Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies. Iros. https://arxiv.org/pdf/1905.09668
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Reinforcement Learning
Daniel, C., van Hoof, H., Peters, J., & Neumann, G. (2016). Probabilistic inference for determining options in reinforcement learning. Machine Learning, 104(2–3), 337–357. https://doi.org/10.1007/s10994-016-5580-x
|
Reinforcement Learning
Herman, M., Gindele, T., Wagner, J., Schmitt, F., & Burgard, W. (2016a). Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics. AISTATS. https://arxiv.org/pdf/1604.03912.pdf
|
Reinforcement Learning
Herman, M., Fischer, V., Gindele, T., & Burgard, W. (2015). Inverse Reinforcement Learning of Behavioral Models for Online-Adapting Navigation Strategies. ICRA. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7139642
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Reinforcement Learning
Bischoff, B., Nguyn-Tuong, D., Lee, I.-H., Streichert, F., & Knoll, A. (2013). Hierarchical Reinforcement Learning for Robot Navigation. ESANN. https://www.in.tum.de/i06/Main/Publications/BischoffESANN13a.pdf
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