Publications

Becker, P., Arenz, O., & Neumann, G. (2020). Expected Information Maximization: Using the I-Projection for Mixture Density Estimation. ICLR. [Pdf]

Chubanov, S. (2020a). A polynomial algorithm for convex quadratic optimization subject to linear inequalities. Discrete Applied Mathematics, 275, 19–28. [Pdf]

Chubanov, S. (2020b). A scaling algorithm for optimizing arbitrary functions over vertices of polytopes. Mathematical Programming, para. 1. [Pdf]

De Avila Belbute-Peres, F., Economon, T., & Kolter, Z. (2020). Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction. ICML. [Pdf]

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]

Elsken, T., Staffler, B., Metzen, J. H., & Hutter, F. (2020). Meta-Learning of Neural Architectures for Few-Shot Learning. CVPR. [Pdf]

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]

Forssell, H., Kharlamov, E., & Thorstensen, E. (2020). On Equivalence and Cores for Incomplete Databases in Open and Closed Worlds. ICDT. [Pdf]

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]

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]

Fröhlich, L., Klenske, E., Vinogradska, J., Daniel, C., & Zeilinger, M. (2020). Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization. AISTATS. [Pdf]

Gad-Elrab, M. H., Stepanova, D., Tran, T.-K., Adel, H., & Weikum, G. (2020). ExCut: Explainable Embedding-based Clustering over Knowledge Graphs. ISWC. [Pdf]

Gargiani, M., Zanelli, A., Dinh, Q. T., Diehl, M., & Hutter, F. (2020). Transferring Optimality Across Data Distributions via Homotopy Methods. ICLR. [Pdf]

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]

Gulshad, S., & Smeulders, A. (2020). Explaining with Counter Visual Attributes and Examples. ICMR. [Pdf]

Hewing, L., Arcari, E., Froehlich, L., & Zeilinger, M. (2020). On Simulation and Trajectory Prediction with Gaussian Process Dynamics. L4DC. [Pdf]

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]

Kipf, T., van der Pol, E., & Welling, M. (2020). Contrastive Learning of Structured World Models. ICLR. [Pdf]

Klenske, E.D. Optimal test pooling for efficient PCR testing of SARS-CoV2. Ir J Med Sci (2020). [Pdf]

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]

Lange, L., Adel, H., & Stroetgen, J. (2020a). Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain. ACL. [Pdf]

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]

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]

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]

Li, S., Huang, Z., Cheng, G., Kharlamov, E., & Gunaratna, K. (2020). Enriching Documents with Compact, Representative, Relevant Knowledge Graphs. IJCAI. [Pdf]

Liu, Q., Chen, Y., Cheng, G., Kharlamov, E., Li, J., & Qu, Y. (2020). Entity Summarization with User Feedback. ESWC. [Pdf]

Maini, P., Wong, E., & Kolter, Z. (2020). Adversarial Robustness Against the Union of Multiple Threat Models. ICML. [Pdf]

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]

Rosenfeld, E., Winston, E., Ravikumar, P., & Kolter, Z. (2020). Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. ICML. [Pdf]

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]

Schönfeld, E., Schiele, B., & Khoreva, A. (2020). A U-Net Based Discriminator for Generative Adversarial Networks. CVPR. [Pdf]

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]

Shi, Y., Cheng, G., & Kharlamov, E. (2020). Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings. WWW. [Pdf]

Sosnovik, I., Szmaja, M., & Smeulders, A. (2020). Scale-Equivariant Steerable Networks. ICLR. [Pdf]

Todescato, M., Carron, A., Carli, R., Pillonetto, G., & Schenato, L. (2020). Efficient spatio-temporal Gaussian regression via Kalman filtering. Automatica, 118, 109032. [Pdf]

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]

Van der Pol, E., Kipf, T., Oliehoek, F., & Welling, M. (2020). Plannable Approximations to MDP Homomorphisms: Equivariance under actions. AAMAS. [Pdf]

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]

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]

Wang, P.-W., Stepanova, D., Domokos, C., & Kolter, J. Z. (2020). Differentiable learning of numerical rules in knowledge graphs. ICLR. [Pdf]

Wöhlke, J., Schmitt, F., & van Hoof, H. (2020). A Performance-Based Start State Curriculum Framework for Reinforcement Learning. AAMAS. [Pdf]

Wong, E., Rice, L., & Kolter, J. Z. (2020a). Fast is better than free: Revisiting adversarial training. ICLR. [Pdf]

Wong, E., Rice, L., & Kolter, Z. (2020b). Overfitting in adversarially robust deep learning. ICML. [Pdf]

Zela, A., Elsken, T., Saikia, T., Marrakchi, Y., Brox, T., & Hutter, F. (2020). Understanding and Robustifying Differentiable Architecture Search. ICLR. [Pdf]

Zela, A., Siems, J., & Hutter, F. (2020). NAS-BENCH-1SHOT1: Benchmarkting and Dissecting One-Shot Neural Architecture Search. ICLR. [Pdf]

Zimmer, C., Driess, D., Meister, M., & Nguyen-Tuong, D. (2020). Adaptive Discretization for Evaluation of Probabilistic Cost Functions. AISTATS. [Pdf]

Agrawal, A., Amos, B., Barratt, S., Boyd, S., Diamond, S., & Kolter, J. Z. (2019). Differentiable Convex Optimization Layers. NeurIPS. [Pdf]

Akrour, R., Pajarinen, J., & Neumann, G. (2019). Projections for Approximate Policy Iteration Algorithms. ICML. [Pdf]

Angerbauer, K., Adel, H., & Vu, N. T. (2019). Automatic Compression of Subtitles with Neural Networks and its Effect on User Experiences. Interspeech. [Pdf]

Arvanitis, G., Hauberg, S., Henning, P., & Schober, M. (2019). Fast and Robust Shortest Paths on Manifolds Learned from Data. AISTATS. [Pdf]

Bai, S., Koltun, V., & Kolter, J. Z. (2019). Deep Equilibrium Models. NeurIPS. [Pdf]

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]

Beggel, L., Pfeiffer, M., & Bischl, B. (2019). Robust Anomaly Detection in Images using Adversarial Autoencoders. ECML. [Pdf]

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]

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.

Blaiotta, C. (2019). Learning Generative Socially Aware Models of Pedestrian Motion. IEEE Robotics and Automation Letters, 4(4), 3433–3440. [Pdf]

Chen, J., Wang, X., Cheng, G., Kharlamow, E., & Qu, Y. (2019). Towards More Usable Dataset Search: From Query Characterization to Snippet Generation. CIKM. [Pdf]

Cohen, J., Rosenfeld, E., & Kolter, Z. (2019). Certified Adversarial Robustness via Randomized Smoothing. ICML. [Pdf]

Delhaisse, B., Rozo, L., & Caldwell, D. G. (2019). PyRoboLearn: A Python Framework for Robot Learning Practitioners. CoRL. [Pdf]

Dikeoulias, I., Strötgen, J., & Razniewski, S. (2019). Epitaph or Breaking News? Analyzing and Predicting the Stability of Knowledge Base Properties. TempWeb. [Pdf]

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]

Doerr, A., Volpp, M., Toussaint, M., Trimpe, S., & Daniel, C. (2019). Trajectory-Based Off-Policy Deep Reinforcement Learning. ICML. [Pdf]

Elsken, T., Metzen, J. H., & Hutter, F. (2019a). Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution. ICLR. [Pdf]

Elsken, T., Metzen, J. H., & Hutter, F. (2019b). Neural Architecture Search: A Survey. Journal of Machine Learning Research 20 (2, 1–21. [Pdf]

Esteban, D., Rozo, L., & Caldwell, D. G. (2019). Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies. Iros. [Pdf]

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]

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]

Gad-Elrab, M., Stepanova, D., Urbani, J., & Weikum, G. (2019a). ExFact: Explaining Facts over Knowledge Graphs and Text. WSDM. [Pdf]

Gad-Elrab, M., Stepanova, D., Urbani, J., & Weikum, G. (2019b). Tracy: Tracing Facts over Knowledge Graphs and Text. WWW. [Pdf]

Garcia, V., Akata, Z., & Welling, M. (2019). GRIN: Graphical Recurrent Inference Networks. NeurIPS. [Pdf]

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]

Guo, M., & Bürger, M. (2019). Predictive Safety Network for Resource-constrained Multi-agent Systems. CoRL. [Pdf]

Gupta, D., Berberich, K., Strötgen, J., & Zeinalipour-Yazti, D. (2019). Generating Semantic Aspects for Queries. ESWC. [Pdf]

Haussmann, M., Hamprecht, F. A., & Kandemir, M. (2019a). Deep Active Learning with Adaptive Acquisition. IJCAI. [Pdf]

Haussmann, M., Hamprecht, F. A., & Kandemir, M. (2019b). Sampling-Free Variational Inference of Bayesian Neural Nets with Variance Backpropagation. UAI. [Pdf]

Hoogeboom, E., Peters, J., van den Berg, R., & Welling, M. (2019). Integer Discrete Flows and Lossless Compression. NeurIPS. [Pdf]

Hoogeboom, E., Van den Berg, R., & Welling, M. (2019). Emerging Convolutions for Generative Normalizing Flows. ICML. [Pdf]

Hoyer, L., Kesper, P., Khoreva, A., & Fischer, V. (2019). Short-Term Prediction and Multi-Camera Fusion on Semantic Grids. ICCV. [Pdf]

Hoyer, L., Munoz, M., Katiyar, P., Khoreva, A., & Fischer, V. (2019). Grid Saliency for Context Explanations of Semantic Segmentation. NeurIPS. [Pdf]

Huang, Y., Rozo, L., Silvério, J., & Caldwell, D. G. (2019). Kernelized movement primitives. The International Journal of Robotics Research, 38(7), 833–852. [Pdf]

Huang, Y., Rozo, L., Silverio, J., & Caldwell, D. G. (2019). Non-parametric Imitation Learning of Robot Motor Skills. ICRA. [Pdf]

Huang, Z., Li, S., Cheng, G., Kharlamov, E., & Qu, Y. (2019). Making Sense of News via Relationship Subgraphs. CIKM. [Pdf]

Jaquier, N., Rozo, L., Calinon, S., & Bürger, M. (2019). Bayesian Optimization Meets Riemannian Manifolds in Robot Learning. CoRL. [Pdf]

John, D., Heuveline, V., & ERROR: No link has been specified! (2019). GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver. ICML. [Pdf]

Kemos, A., Adel, H., & Schütze, H. (2019). Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging. NAACL. [Pdf]

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]

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]

Koch, M., Spies, M., & Bürger, M. (2019). Trust Regions for Safe Sampling-Based Model Predictive Control. ICRA. [Pdf]

Köhler, J., Autenrieth, M., & Beluch, W. (2019). Uncertainty Based Detection and Relabeling of Noisy Image Labels. CVPR. [Pdf]

Kusumoto, R., Palmieri, L., Spies, M., Csiszar, A., & Arras, K. O. (2019). Informed Information Theoretic Model Predictive Control. ICRA. [Pdf]

Lange, L., Adel, H., & Stroetgen, J. (2019). NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection. BioNLP. [Pdf]

Lange, L., Adel, H., & Strötgen, J. (2019). NLNDE: The Neither-Language-Nor-Domain-Experts’ Way of Spanish Medical Document De-Identification. IberLEF. [Pdf]

Lange, L., Alonso, O., & Strötgen, J. (2019). The Power of Temporal Features for Classifying News Articles. TempWeb. [Pdf]

Lange, L., Hedderich, M. A., & Klakow, D. (2019). Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels. EMNLP. [Pdf]

Li, B., Schmidt, F. R., & Kolter, Z. (2019). Adversarial camera stickers: A physical camera-based attack on deep learning systems. ICML. [Pdf]

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]

Look, A., & Kandemir, M. (2019, December). Differential Bayesian Neural Nets [Workshop: Poster]. NeurIPS, Vancouver, Canada. [Pdf]

Louizos, C., Shi, X., & Welling, M. (2019). The Functional Neural Process. NeurIPS. [Pdf]

Mailis, T., Kotidis, Y., Nikolopoulos, V., Kharlamov, E., Horrocks, I., & Ioannidis, Y. (2019a). An Efficient Index for RDF Query Containment. SIGMOD. [Pdf]

Mailis, T., Kotidis, Y., Nikolopoulos, V., Kharlamov, E., Horrocks, I., & Ioannidis, Y. (2019b). Mv-index: An Efficient Index for Graph-Query Containment. ISWC. [Pdf]

Manek, G., & Kolter, J. Z. (2019). Learning Stable Deep Dynamics Models. NeurIPS. [Pdf]

McHardy, R., Adel, H., & Klinger, R. (2019). Adversarial Training for Satire Detection: Controlling for Confounding Variables. NAACL. [Pdf]

Mehdi, A., Kharlamov, E., Stepanova, D., Loesch, F., & Gonzales, I. G. (2019). Towards Semantic Integration of Bosch Manufacturing Data. ISCW (Industry Track). [Pdf]

Mettes, P., van der Pol, E., & Snoek, C. (2019). Hyperspherical Prototype Networks. NeurIPS. [Pdf]

Mummadi, C. K., Brox, T., & Metzen, J. H. (2019). Defending against universal perturbations with shared adversarial training. ICCV. [Pdf]

Nagarajan, V., & Kolter, J. Z. (2019). Uniform convergence may be unable to explain generalization in deep learning. NeurIPS. [Pdf]

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]

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]

Rozo, L. (2019). Interactive Trajectory Adaptation through Force-guided Bayesian Optimization. IROS. [Pdf]

Savkovic, O., Kharlamov, E., & Lamparter, S. (2019). Validation of SHACL Constraints over KGs with OWL 2 Ontologies via Rewriting. ESWC. [Pdf]

Schönfeld, E., Ebrahimi, S., Sinha, S., Darrall, T., & Akata, Z. (2019). Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders. CVPR. [Pdf]

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]

Schwenkel, L., Guo, M., & Bürger, M. (2019). Optimizing Sequences of Probabilistic Manipulation Skills Learned from Demonstration. CoRL. [Pdf]

Silverio, J., Huang, Y., Abu-dakka, F., Rozo, L., & Caldwell, D. G. (2019). Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives. IROS. [Pdf]

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]

Trouleau, W., Etesami, J., Grossglauser, M., Kiyavash, M., & Thiran, P. (2019). Learning Hawkes Processes Under Synchronization Noise. ICML. [Pdf]

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]

Wang, P.-W., Donti, P., Wilder, B., & Kolter, Z. (2019). SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver. ICML. [Pdf]

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]

Wang, X., Cheng, G., & Kharlamov, E. (2019). Towards Multi-Facet Snippets for Dataset Search. PROFILES. [Pdf]

Waniek, N. (2020). Transition Scale-Spaces: A Computational Theory for the Discretized Entorhinal Cortex. Neural Computation, 32(2), 330–394. [Pdf]

Wong, E., Schmidt, F. R., & Kolter, Z. (2019). Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. ICML. [Pdf]

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]

Zhang, D., & Khoreva, A. (2019). Progressive Augmentation of GANs. NeurIPS. [Pdf]

Zheleznyakov, D., Kharlamov, E., Nutt, W., & Calvanese, D. (2019). On Expansion and Contraction of DL-Lite Knowledge Bases. SSRN Electronic Journal, 1–23. [Pdf]

Achterhold, J., Köhler, J. M., Schmeink, A., & Genewein, T. (2018). Variational Network Quantization. ICLR. [Pdf]

Agarwal, P., Strötgen, J., del Corro, L., Hoffart, J., & Weikum, G. (2018). diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora. ACL. [Pdf]

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]

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]

Domokos, C., Schmidt, F. R., & Cremers, D. (2018). MRF Optimization with Separable Convex Prior on Partially Ordered Labels. ECCV. [Pdf]

Domokos, C., Schmidt, F. R., & Cremers, D. (2018). MRF Optimization with Separable Convex Prior on Partially Ordered Labels. ECCV. [Pdf]

Estellers, V., Schmidt, F. R., & Cremers, D. (2018). Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis. 3DV. [Pdf]

Etesami, J., Habibnia, A., & Kiyavash, N. (2018). Econometric Modeling of Systemic Risk: A Time Series Approach. KDD. [Pdf]

Fischer, V., Pfeil, T., & Köhler, J. (2018). The streaming rollout of deep networks - towards fully model-parallel execution. NIPS. [Pdf]

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]

Pfeiffer, M., & Pfeil, T. (2018). Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience, 12, para. 1. [Pdf]

Salehkaleybar, S., Etesami, J., Kiyavash, N., & Zhang, K. (2018). Learning Vector Autoregressive Models with Latent Processes. AAAI. [Pdf]

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]

Waniek, N. (2018). Hexagonal Grid Fields Optimally Encode Transitions in Spatiotemporal Sequences. Neural Computation, 30(10), 2691–2725. [Pdf]

Wong, E., Schmidt, F. R., Metzen, J. H., & Kolter, Z. (2018). Scaling provable adversarial defenses. NIPS. [Pdf]

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]

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]

Fischer, V., Kumar, M. C., Metzen, J. H., & Brox, T. (2017). Adversarial examples for semantic image segmentation. ICLR. [Pdf]

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]

Heit, J., Liu, J., & Shah, M. (2017). An Architecture for the Deployment of Statistical Models for the Big Data Era. IEEE. [Pdf]

Meister, M., & Steinwart, I. (2017). Optimal Learning Rates for Localized SVMs. JMLR. [Pdf]

Metzen, J. H., Genewein, T., Fischer, V., & Bischoff, B. (2017). On Detecting Adversarial Perturbations. ICLR. [Pdf]

Metzen, J. H., Kumar, M. C., Brox, T., & Fischer, V. (2017). Universal Adversarial Perturbations Against Semantic Image Segmentation. ICCV. [Pdf]

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