Publications

Yumeng Li, William Beluch, Margret Keuper, Dan Zhang, Anna Khoreva (2025). VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis. PDF

Alexander Timans, Christoph-Nikolas Straehle*, Kaspar Sakmann, Christian Nasseth, Eric Nalisnick (2025). Max-Rank: Efficient Multiple Testing for Conformal Prediction. PDF

He, Y., Xiong, B., Hernández, D., Zhu, Y., Kharlamov, E. & Staab, S. (2025). DAGE: DAG Query Answering via Relational Combinator with Logical Constraints. WWW. PDF

Flynn, H. & Reeb, D. (2025). Tighter Confidence Bounds for Sequential Kernel Regression. AISTATS. PDF

Zhou, D., Kharlamov, E. & Kostylev, E.V. (2025). GLoRa: A Benchmark to Evaluate the Ability to Learn Long-Range Dependencies in Graphs. ICLR.

Zhu, Y., Potyka, N., Pan, J., Xiong, B., He, Y., Kharlamov, E. & Staab, S. (2025). Conformalized Answer Set Prediction for Knowledge Graph Embedding. NAACL. PDF

Akinwande, V., Jiang, Y., Sam, D. & Kolter, J. Z. (2024). Understanding prompt engineering may not require rethinking generalization. ICLR. PDF

Baek, C., Kolter, J. Z. & Raghunathan, A. (2024). Why is SAM Robust to Label Noise? ICLR. PDF

Beik-Mohammedi, H., Hauberg, S., Arvanitidis, G., Figueroa, N., Neumann, G. & Rozo, L. (2024). Neural Contractive Dynamical Systems. PDF

Bini, M., Roth, K., Akata, Z. & Khoreva, A. (2024). ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections. ICML. PDF

Cheng, Z., Hao, Z., Xiaoqiang, W., Huang, J., Wu, Y., Liu, X., Zhao, Y., Songming, L. & Su, H. (2024). Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations. ICML. PDF

Chen, Q., Luo, W., Huang, Z., Lin, T., Wang, X., Soylu, A., Ell, B., Zhou, B., Kharlamov, E. & Cheng, G. (2024). ACORDAR 2.0: A Test Collection for Ad Hoc Dataset Retrieval with Densely Pooled Datasets and Question-Style Queries. SIGIR.

Eiter, T., Geibinger, T., Ruiz, N.H., Musliu, N., Oetsch, J., Pfliegler, D. & Stepanova, D. (2024). Adaptive large-neighbourhood search for optimisation in answer-set programming. AIJ. PDF

Ensinger, K., Tagliapietra, N., Ziesche, S. & Trimpe, S. (2024). Exact Inference for Continuous-Time Gaussian Process Dynamics. PDF

Ensinger, K., Ziesche, S. & Trimpe, S. (2024). Learning Hybrid Dynamics Models with Simulator-Informed Latent States. PDF

Hoffmann, D., Schrodi, S., Behrmann, N., Fischer, V. & Brox, T. (2024). Eureka-Moments in Transformers: Multi-Step Tasks Reveal Softmax Induced Optimization Problems. ICML. PDF

He, Y., Murata, N., Lai, C., Takida, Y., Uesaka, T., Kim, D., Liao, W., Mitsufuji, Y., Kolter, J., Z., Salakhutdinov R. & Ermon, S. (2024). Manifold Preserving Guided Diffusion. ICLR. PDF

Huang, H., Peng, S., Zhang, D. & Geiger, A. (2024). Renovating Names in Open-Vocabulary Segmentation Benchmarks. NeurIPs. PDF

Jaquier, N., Rozo, L., González-Duque, M., Borovitskiy, V. & Asfour, T. (2024). Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds. ICML. PDF

Jazbec, M., Forré, P., Mandt, S., Zhang, D. & Nalisnick, E. (2024). Early-Exit Neural Networks with Nested Prediction Sets. UAI. PDF

Jazbec, M., Timans, A., Hadži Veljković, T., Sakmann, K., Zhang, D., Naesseth, C. & Nalisnick, E. (2024). Fast yet Safe: Early-Exiting with Risk Control. NeurIPs. PDF

Jiang, Y., Baek, C. & Kolter, J. Z. (2024). On the Joint Interaction of Models, Data, and Features. ICLR. PDF

Kälble, J., Wirges, S., Tatarchenko, M. & Ilg, E. (2024). Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory. PDF

Koch, S., Vaskevicius, N., Colosi, M., Hermosilla, P & Ropinski, T. (2024). Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships. CVPR. PDF

Li, Y., Keuper, M., Zhang, D. & Khoreva, A. (2024). Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive. ICLR. PDF

Mai, H. T., Chu, C. X. & Paulheim, H. (2024). Do LLMs Really Adapt to Domains? An Ontology Learning Perspective. ISWC. PDF

Maini, P., Goyal, S., Lipton, Z., Kolter, J. Z. & Raghunathan, A. (2024). T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. ICLR. PDF

Öcal, B. M., Tatarchenko, M., Karaoğlu, S. & Gevers, T. (2024). SceneTeller: Language-to-3D Scene Generation. ECCV. PDF

Pan, C., Yaman, B., Nesti, T., Mallik, A., Allievi, A., Velipasalar, S. & Ren, L. (2024). VLP: Vision Language Planning for Autonomous Driving. CVPR. PDF

Pan, C., Yaman, B., Velipasalar, S. & Ren, L. (2024). CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow. CVPR. PDF

Pan, J., Falkener, S., Berkenkamp, F. & Vanschoren, J. (2024). MALIBO: Meta-learning for Likelihood-free Bayesian Optimization. ICML. PDF

Potyka, N., Zhu, Y., He, Y., Kharlamov, E. & Staab, S. (2024). Robust Knowledge Extraction from Large Language Models using Social Choice Theory. AAMAS. PDF

Schneider, M., Krug, R., Vaskevicius, N., Palmieri, L. & Boedecker, J. (2024). The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning. NeurIPs.

Schrader, T., Lange, L., Razniewski, S. & Friedrich, A. (2024). QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios. EMNLP. PDF

Sokota, S., Farina, G., Wu, D., Hu, W., Wang, K., Kolter, J. Z. & Brown, N. (2024). The Update Equivalence Framework for Decision-Time Planning. ICLR. PDF

Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. (2024). A Simple and Effective Pruning Approach for Large Language Models. ICLR. PDF

Tebbe, J., Zimmer, C., Steland, A., Lange-Hegermann, M. & Mies, F. (2024). Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning. AISTATS.

Tighineanu, P., Grossberger, L., Baireuther, P., Skubch, K., Falkner, S., Vinogradska, J. & Berkenkamp, F. Scalable (2024). Meta-Learning with Gaussian Processes. AISTATS. PDF

Wang, J., Laube, K. A., Li, Y., Hendrik Metzen, J., Cheng, S., Borges, J. & Khoreva, A. (2024). Label-free Neural Semantic Image Synthesis. ECCV.

Yunjie, H., Hernandez, D., Nayyeri, M., Xiong, B., Yuqicheng, Z., Kharlamov, E. & Staab, S. (2024). Generating SROI Ontologies via Knowledge Graph Query Embedding Learning. ECAI. PDF

Yuqicheng, Z., Nico, P., Nayyeri, M., Xiong, B., Yu, H., Kharlamov, E. & Staab, S. (2024). Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction. EMNLP. PDF

Zhai, R., Liu, B., Risteski, A., Kolter, J. Z. & Ravikumar, P. (2024). Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression. ICLR. PDF

Zhao, H., Yang, B., Cen, Y., Ren, J., Zhang, C., Dong, Y., Kharlamov, E., Zhao, S. & Tang, J. (2024). Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs. KDD.

Zhang, M., Gautam, V., Wang, M., Alabi, J., Shen, X., Klakow, D & Mosbach, M. (2024). The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis. ACL. PDF

Zhuang, Z., Nicolae, M. & Fritz, M. (2024). Stealthy Imitation: Reward-guided Environment-free Policy Stealing. ICML. PDF

Andresel, M., Kien, T., Domokos, C., Minervini, P. & Stepanova, D. (2023). Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs. CIKM

Beik-Mohammadi, H., Hauberg, S., Arvanitidis, G., Neumann, G. & Rozo, L. (2023). Reactive Motion Generation on Learned Riemannian Manifolds. IJRR. PDF

Bitzer, M., Meister, M. & Zimmer, C. (2023). Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels. UAI.

Bitzer, M., Meister, M. & Zimmer, C. (2023). Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems. AISTATS.

Bjerke, M., Schott, L., Jensen, C., Battistin, C., Klindt, D. & Dunn, B. (2023). Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. ICLR. PDF

Carlini, N., Kolter, J. Z., Tramer, F., Dvijotham, K. D., Rice, L. & Sun, M. (2023). (Certified!!) Adversarial Robustness for Free!. ICLR. PDF

Chen, B., Zhang, J., Zhang, X., Dong, Y., Song, J., Zhang, P., Xu, K., Kharlamov, E. & Tang, J. (2023). GCCAD: Graph Contrastive Coding for Anomaly Detection. TKDE.

Chu, C., Gad-Elrab, M., Tran, T., Schiller, M., Kharlamov, E. & Stepanova D. (2023) Supplier Optimization at Bosch with Knowledge Graphs and Answer Set Programming. ESWC.

Chubanov, S. (2023). On the complexity of PAC learning in Hilbert spaces. AAAI.

Cui, P., Zhang, D., Deng, Z., Dong, Y. & Zhu, J. (2023). Learning Sample Difficulty from Pre-trained Models for Reliable Prediction. NeurIPS. PDF

Cohen, L., Mansour, Y. & Moshkovitz, M. (2023).Finding Safe Zones of Markov Decision Processes Policies. NeurIPS. PDF

De Avila Belbute-Peres, F. & Kolter, J. Z. (2023). Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth. ICLR. PDF

Ensinger, K., Ziesche, S., Rakitsch, B., Tiemann, M. & Trimpe, S. (2023). Combining Slow and Fast: Complementary Filtering for Dynamics Learning. AAAI.

Flynn, H., Reeb, D., Kandemir, M. & Peters, J. (2023). PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. TPAMI. PDF

Flynn, H., Reeb, D., Kandemir, M. & Peters, J. (2023). Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. NeurIPS. PDF

Gao, N., Ngo, V.A., Ziesche, H. & Neumann, G. (2023). SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects. CoRL. PDF

Gruner, T., Belousov, B., Muratore, F., Palenicek, D. & Peters, J. (2023). Pseudo-Likelihood Inference. NeurIPS.

He, Y., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E. & Staab, S. (2023). Can Pattern Learning Enhance Complex Logical Query Answering?. ISWC.

Hou, Z., He, Y., Cen, Y., Liu, X., Dong, Y., Kharlamov, E. & Tang, J. (2023). GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner. WWW.

Huang, H., Geiger, A. & Zhang, D. (2023). GOOD: Exploring geometric cues for detecting objects in an open world. ICLR. PDF

Hung, C., Willmott, D. & Kolter, J. Z.(2023). TADA - Efficient Task-Agnostic Domain Adaptation for Transformers. ACL.

Ismaeil, Y., Stepanova, D., Kien, T. & Bloeckeel, H. (2023). Feabi: A Feature Selection-based Framework for Interpreting Knowledge Graph Embeddings. ISWC.

Jazbec, M., Allingham, J., Zhang, D. & Nalisnick, E. (2023): Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. NeurIPs. PDF

Klironomos, A., Zhou, B., Tan, Z., Zheng, Z., Mohamed, G., Paulheim, H. & Kharlamov, E. (2023). ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics. ESWC.

Lange, L., Stroetgen, J., Adel, H. & Klakow, D. (2023). Multilingual Normalization of Temporal Expressions with Masked Language Models. EACL.

Li, A., Qiu, C., Kloft, M., Smyth, P., Mandt, S. & Rudolph, M. (2023). Deep anomaly detection under labeling budget constraints. ICML. PDF.

Li, A., Qiu, C., Kloft, M., Smyth, P., Mandt, S. & Rudolph, M. (2023). Zero-Shot Batch-Level Anomaly Detection. NeurIPS. PDF.

Li, Y., Zhang, D., Keuper, M. & Khoreva, A. (2023). Intra-Source Style Augmentation for Improved Domain Generalization.WACV. PDF

Li, Y., Zhang, D., Keuper, M. & Khoreva, A. (2023). Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization. IJCV.

Luis, C. E., Bottero, A. G., Vinogradska, J., Berkenkamp, F., & Peters, J. (2023). Model-Base Uncertainty in Value Functions. AISTATS.

Mai, T., Ismaeil, Y., Tran, T., Blockeel, H. & Stepanova, D. (2023). Look beyond the Surface: A Demo for Explaining Knowledge Graph Embeddings and Entity Similarity. ISWC.

Metzen, J., Hutmacher, R., Hua, N., Boreiko, V., & Zhang, D.(2023). Identification of Systematic Errors of Image Classifiers on Rare Subgroups. ICCV.

Mohan, R., Elsken, T., Zela, A., Metzen, J. H., Staffler, B., Brox, T., Valada, A. & Hutter, F. (2023). Neural Architecture Search for Dense Prediction Tasks in Computer Vision. IJCV.

Müller, J., Radev, S., Schmier, R., Draxler, F., Rother, C. & Koethe, U. (2023). Finding Competence Regions in Domain Generalization. TMLR. PDF

Nurlanov, Z., Schmidt, F. R. & Bernard, F. (2023). Universe Points Representation Learning for Partial Multi-Graph Matching. AAAI. PDF

Ott, K., Tiemann, M, Hennig, P. & Briol, F. (2023). Bayesian Numerical Integration with Neural Networks. UAI. PDF

Ott, K., Betz, P., Stepanova, D., Gad-Elrab, M., Meilicke, C. & Stuckenschmidt, H. (2023). Rule-based Knowledge Graph Completion with Canonical Models. CIKM.

Qiu, Z., Liu, W., Feng, H., Xue, Y., Feng, Y., Liu, Z., Zhang, D., Weller, A. & Schölkopf, B. (2023). Controlling Text-to-Image Diffusion by Orthogonal Finetuning. NeurIPs. PDF

Rauch, C., Long, R., Ivan, V. & Vijayakumar, S. (2023). Sparse-Dense Motion Modelling and Tracking for Manipulation Without Prior Object Models. ICRA.

Reeb, D., Patel, K., Barsim, K., Schiegg, M. & Gerwinn, S. (2023). Validation of Composite Systems by Disrepancy Propagation. UAI. PDF

Schmier, R., Köthe, U. & Straehle, C. (2023). Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data. PDF

Schoenfeld, E., Borges, J., Schiele, B. & Khoreva, A. (2023) Discovering Class-Specific GAN Controls for Semantic Image Synthesis. CPR workshop for "Generative Models for Computer Vision”. PDF

Schroeder de Witt, C., Sokota, S., Kolter, J. Z., Foerster, J. N. & Strohmeier, M. (2023). Perfectly Secure Steganography Using Minimum Entropy Coupling. ICLR. PDF

Seligmann, F., Becker, P., Volpp, M. & Neumann, G. (2023). Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift. NeurIPs. PDF

Shala, G., Elsken, T., Hutter, F. & Grabocka, J. (2023). Transfer NAS with Meta-learned Bayesian Surrogates. ICLR. PDF

Sokota, S., D'Orazio, R., Kolter, J. Z., Loizou, N., Lanctot, M., Mitliagkas, J., Brown, N. & Kroer, C. (2023). A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. ICLR. PDF

Song, Y., Keller, T., Sebe, N. & Welling, M. (2023). Flow Factorized Representation Learning. NeurIPS. PDF

Sushko, V., Zhang, D. , Gall, J. & Khoreva, A. (2023). One-Shot Synthesis of Images and Segmentation Masks. WACV. PDF

Sushko, V., Wang, R. & Gall, J. (2023). Smoothness Similarity Regularization for Few-Shot GAN Adaptation. ICCV

Tan, Z., Zhou, B., Zheng, Z., Savkovic, O., Huang, Z., Grangel Gonzalez, I., Soylu, A. & Kharlamov, E. (2023). Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case. ISWC.

Tan, Z., Zheng, Z., Klironomos, A., Gad-Elrab, M., Xiao, G., Soylu, A., Kharlamov, E. & Zhou, B. Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring. ISWC.

Taranovic, A., Kupcsik, A. G., Freymuth, N. & Neumann, G. (2023). Adversarial Imitation Learning with Preferences. ICLR. PDF

Tatarchenko, M. & Rambach, K. (2023). Histogram-based Deep Learning for Automotive Radar. PDF

Trockman, A., Willmott, D.& Kolter, J. Z. (2023). Understanding the Covariance Structure of Convolutional Filters. ICLR. PDF

Veseli, B., Singhania, S., Razniewski, S. & Weikum, G. (2023). Evaluating Language Models for Knowledge Base Completion. ESWC. PDF

Volpp, M., Dahlinger, P., Becker, P., Daniel, C.& Neuma, G. (2023). Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference. ICLR. PDF

Wang, M., Adel, H., Lange, L., Strötgen, J. & Schütze, H. (2023). GradSim: Gradient-Based Language Grouping for Effective Multilingual Training. EMNLP.

Wang, X., Cheng, G., Pan, J., Kharlamov, E.& Qu, Y. (2023). BANDAR: Benchmarking Snippet Generation Algorithms for (RDF) Dataset Search. TKDE

Yatsura, M., Sakmann, K., Hua, N. G., Hein, M. & Metzen, J. H. (2023). Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation. ICLR. PDF

Zhai, R., Dan, C., Kolter, J. Z.& Ravikumar, P. K. (2023). Understanding Why Generalized Reweighting Does Not Improve Over ERM. ICLR. PDF

Zhang, D., Zhu, Y., Dong, Y., Wang, Y., Feng, W., Kharlamov, E. & Tang, J. (2023). ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation. WWW.

Zhang, F., Liu, X., Tang, J., Dong, Y., Yao, P., Zhang, J., Gu, X., Wang, Y., Kharlamov, E., Shao, B., Li, R. & Wang, K. (2023). OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs. TKDE.

Zheng, Z., Zhou, B., Tan, Z., Savkovic, O., Rincon-Yanez, D., Nikolov, N., Roman, D., Soylu, A. & Kharlamov, E. (2023). Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch. ISWC.

Zheng, Z., Savkovic, O., Nikolov, N., Luu, H., Soylu, A., Kharlamov, E. & Zhou, B. (2023). Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration. ISWC.

Zhou, B., Nikolov, N., Zheng, Z., Luo, X., Savkovic, O., Roman, D., Soylu, A. & Kharlamov, E. (2023). Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case. ISWC.

Zhu, Y., Potyka, N., Xiong, B., Tran, T., Nayyeri, M., Staab, S. & Kharlamov, E. (2023). Towards Statistical Reasoning with Ontology Embeddings. ISWC.

Ziesche, H. & Rozo, L. (2023). Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies. NeurIPS.

Anil, C., Pokle, A., Liang, K., Treutlein, J., Wu, Y., Bai, S., Kolter, J. Z., & Grosse, R. B. (2022). Path Independent Equilibrium Models Can Better Exploit Test-Time Computation. NeurIPS.

Adrian, D., Kupcsik, A., Spies, M., & Neumann H. (2022). Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation. ICRA.

Arnaout, H., Tran, T.-K., Stepanova, D., Gad-Elrab, M.H., Razniewski, S., & Weikum G. (2022). Utilizing Language Models for Knowledge Graph Repair. WWW.

Baek, C., Jiang, Y., Raghunathan, A., & Kolter, J. Z. (2022). Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift. NeurIPS.

Bai, S., Koltun, V., & Kolter, J. Z. (2022). Neural Deep Equilibrium Solvers. ICLR.

Bansal, A., Stoll, D., Janowski, M., Zela, A., & Hutter, F. (2022). JAHS-Bench-201: A foundation for research on joint architecture and hyperparameter search. NeurIPS. PDF

Bitzer, M., Meister, M., & Zimmer, C. (2022). Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport. NeurIPS.

Bottero, A. G., Luis, C. E., Vinogradska, J., Berkenkamp, F. & Peters, J. (2022). Information Theoretic Safe Exploration with Gaussian Processes. NeurIPS.

Di Castro, S., Mannot, S., & Di Castro, D. (2022). Analysis of Stochastic Processes through Replay Buffers. ICML.

Duffhauss, F., Ngo, A. V., Ziesche, H., & Neumann, G. (2022). FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion. ECCV.

Eiter, T., Geibinger, T., Higuera, N., Musliu, N., Oetsch, J., & Stepanova, D. (2022). Large-Neighbourhood Search for Optimisation in Answer-Set Solving. AAAI.

Eiter, T., Geibinger, T., Musliu, N., Oetsch, N. J., & Stepanova, D. (2022). Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling. AAAI.

Eiter, T., Geibinger, T., Musliu, N., Oetsch, N. J., & Stepanova D. (2022). ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser. KR.

Feng, W., Dong, Y., Tinglin, H., Yin, Z., Cheng, X., Kharlamov, E., & Tang, J. (2022). GRAND+: Scalable Graph-based Semi-Supervised Learning with Better Generalization. WWW.

Ferreira, F., Nierhoff, T., Sälinger, A., & Hutter, F. (2022). Learning synthetic environments and reward networks for reinforcement learning. ICLR. PDF

Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., & Hutter, F. (2022). Auto-sklearn 2.0: Hands-free automl via meta-learning. JMLR. PDF

Freymuth, N., Schreiber, N., Taranovic, A., Becker, P., & Neumann, G. (2022). Inferring Versatile Plans from Demonstrations by Matching Geometric Features. CoRL. PDF

Fröhlich, L., Lefarov, M., Zeilinger, M., & Berkenkamp, F. (2022). On-Policy Model Errors in Reinforcement Learning. ICLR. PDF

Gao, N., Ziesche, H., Vien, N.A., Volpp, M., & Neumann G. (2022). What Matters For Meta-Learning Vision Regression Tasks?. CVPR.

Geiger, P., & Straehle, C.-N. (2022). Fail-Safe Adversarial Generative Imitation Learning. TMLR. PDF

Goyal, S., Sun, M., Raghunathan, A., & Kolter, J. Z. (2022). Test Time Adaptation via Conjugate Pseudo-labels. NeurIPS. PDF

Graf, C., Adrian, D. B., Weil, J., Gabriel, M., Schillinger, P., Spies, M., Neumann, H., & Kupcsik, A. G. (2022). Learning Dense Visual Descriptors using Image Augmentations for Robot Manipulation Tasks. CoRL. PDF

Ho, V. T., Stepanova, D., Milchevski, D., Stroetgen, J., & Weikum, G. (2022). Enhancing Knowledge Bases with Quantity Facts. WWW.

Hoffmann, D. T., Behrmann, N., Gall, J., Brox, T., & Noroozi, M. (2022). Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives. AAAI.

Jiang, Y., Liu, E. Z., Eysenbach, B., Kolter, J. Z., & Finn, C. (2022). Learning Options via Compression. NeurIPS.

Jiang, Y., Nagarajan, V., Baek, C., & Kolter, J. Z. (2022). Assessing Generalization of SGD via Disagreement. ICLR.

Kosman, E., & Di Castro, D. (2022). GraphVid: It Only Takes a Few Nodes to Understand a Video. ECCV. PDF

Krishnakumar, A., White, C., Zela, A., Tu, R., Safari, M., & Hutter, F. (2022). Nas-bench-suite-zero: Accelerating research on zero cost proxies. NeurIPS. PDF

Levinkov, E., Kardoost, A., Andres, B., & Keuper M. (2022). Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation. TPAMI.

Li, C. Y., Rakitsch, B., & Zimmer, C. (2022). Safe Active Learning for Multi-Output Gaussian Processes. AISTATS.

Lin, T., Chen, Q., Cheng, G., Soylu, A., Ell, B., Zhao, R., Shi, Q., Wang, X., Gu, Y., & Kharlamov, E. (2022). ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval. SIGIR.

Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Ruhkopf, T., Sass, R., & Hutter, F. (2022). Smac3: A versatile bayesian optimization package for hyperparameter optimization. JMLR. PDF

Lindinger, J., Rakitsch, B., & Lippert, C. (2022). Laplace Approximated Gaussian Process State-Space Models. UAI. PDF

Liu, X., Hong, H., Wang, X., Chen, Z., Kharlamov, E., Dong, Y., & Tang, J. (2022). SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs. WWW.

Look, A., Rakitsch, B., Kandemir, M., & Peters, J. (2022). A Deterministic Approximation to Neural SDEs. PAMI.

Lovisotto, G., Finnie, N., Munoz, M., Mummadi, C. K., & Metzen, J. H. (2022). Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness. CVPR.

Maini, P., Garg, S., Lipton, Z. C., & Kolter, J. Z. (2022). Characterizing Datapoints via Second-Split Forgetting. NeurIPS.

Manek, G., & Kolter, J. Z. (2022). The Pitfalls of Regularization in Off-Policy TD Learning. NeurIPS.

Mehta, Y., White, C., Zela, A., Krishnakumar, A., Zabergja, G., Moradian, S., Safari, M., Yu, K., & Hutter, F. (2022). Nas-bench-suite: Nas evaluation is (now) surprisingly easy. ICLR. PDF

Moskalev, A., Sosnovik, I., Fischer, V., & Smeulders, A. (2022). Contrasting quadratic assignments for set-based representation learning. ECCV. PDF

Müller, S., Hollmann, N., Arango, S. P., Grabocka, J., & Hutter, F. (2022). Transformers can do bayesian inference. ICLR. PDF

Nonnenmacher, M., Pfeil, T., Steinwart, I., & Reeb, D. (2022). SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. ICLR.

Nonnenmacher, M., Oldenburg, L., Steinwart, I., & Reeb, D. (2022). Utilizing Expert Features for Contrastive Learning of Time-Series Representations. ICML.

Öztürk, E., Ferreira, F., Jomaa, H., Schmidt-Thieme, L., Grabocka, J., & Hutter, F. (2022). Zero-shot automl with pretrained models. ICML. PDF

Otto, F., Celik, O., Zhou, H., Ziesche, H., Ngo, A. V., & Neumann, G. (2022). Deep Black-Box Reinforcement Learning with Movement Primitives. CoRL. PDF

Pokle, A., Geng, Z., & Kolter, J. Z. (2022). Deep Equilibrium Approaches to Diffusion Models. NeurIPS.

Pujari, S., Strötgen, J., Giereth, M., Gertz, M., & Friedrich, A. (2022). Three Real-World Datasets and Neural Computational Models for Classification Tasks in Patent Landscaping. EMNLP.

Pujari, S., Mantiuk, F., Giereth, M., Stroetgen, J., & Friedrich, A. (2022). Evaluating Neural Multi-Field Document Representations for Patent Classification. BIR.

Qiu, C., Kloft, M., Mandt, S., & Rudolph, M. (2022). Raising the bar in graph-level anomaly detection. IJCAI.

Qiu, C., Li, A., Kloft, M., Rudolph, M., & Mandt, S. (2022). Latent Outlier Exposure for Anomaly Detection with Contaminated Data. ICML. PDF

Saseendran, A., Skubch, K., & Keuper, M. (2022). Trading off Image Quality for Robustness is not Necessary with Deterministic Autoencoders. NeurIPS.

Schirmer, M., Eltayeb, M., Lessmann, S., & Rudolph, M. (2022). Modeling Irregular Time Series with Continuous Recurrent Units. ICML. PDF

Sepliarskaia, A., Moskalev, A., Sosnovik, I., & Smeulders, A. (2022). LieGG: Studying learned Lie group generators. NeurIPS. PDF

Shi, Z., Wang, Y., Zhang, H., Kolter, J. Z., & Hsieh, C. (2022). Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. NeurIPS.

Sokota, S., Hu, H., Wu, D. J., Kolter, J. Z., Foerster, J. N., & Brown, N. (2022). A Fine-Tuning Approach to Belief State Modeling. ICLR.

Sushko, V., Schoenfeld, E., Zhang, D., Gall, J., Schiele, B., & Khoreva, A. (2022). OASIS: Only Adversarial Supervision for Semantic Image Synthesis. IJCV.

Tighineanu, P., Skubch, K., Baireuther, P., Reiss, A., Berkenkamp, F., & Vinogradska, J. (2022). Transfer Learning with Gaussian Processes for Bayesian Optimization. AISTATS.

Wei, C. & Kolter, J. Z. (2022). Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation. ICLR.

Wöhlke, J., Schmitt, F., & van Hoof, H. (2022). Value Refinement Network (VRN). IJCAI.

Xiong, B., Potyka, N., Tran, T., Nayyeri, M., & Staab, S. (2022). Faithful Embeddings for EL++ Knowledge Bases. ISWC.

Yildiz, C., Kandemir, M., & Rakitsch, B. (2022). Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs. NeurIPS.

Youmna, I., Stepanova, D., Tran, T., Saranrittichai, P., Domokos, C., & Blockeel, H. (2022). Towards Neural Network Interpretability Using Commonsense Knowledge Graphs. ISWC.

Zela, A., Siems, J. N., Zimmer, L., Lukasik, J., Keuper, M., & Hutter, F. (2022). Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks. ICLR. PDF

Zhang, H., Wang, S., Xu, K., Li, L., Li, B., Jana, S., Hsieh, C., & Kolter, J. Z. (2022). General Cutting Planes for Bound-Propagation-Based Neural Network Verification. NeurIPS.

Zhang, J., Beik-Mohammadi, H., & Rozo, L. (2022). Learning Riemannian Stable Dynamical Systems via Diffeomorphisms. CoRL. PDF

Behrmann, N., Gall, J., & Noroozi, M. (2021). Unsupervised Video Representation Learning by Bidirectional Feature Prediction. WACV. [Pdf]

Chen, H., Deng, S., Zhang, W., Xu, Z., Li, J., & Kharlamov, E. (2021). Neural symbolic reasoning with knowledge graphs: Knowledge extraction, relational reasoning, and inconsistency checking. Fundamental Research, 1(5), 565–573. PDF

Chowdhury, S. N., Wickramarachchi, R., Gad-Elrab, M. H., Stepanova, D., & Henson, C. A. (2021). Towards Leveraging Commonsense Knowledge for Autonomous Driving. ISWC. PDF

Cohen, J., Kaur, S., Li, Y., Kolter, J. Z., & Talwalkar, A. (2021). Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability. ICLR. [Pdf]

Di Castro, S. S., Mannor, S., & Di Castro, D. (2021). Sim and Real: Better Together. NeurIPS. [Pdf]

Donti, P. L., Agarwal, A., Bedmutha, N. V., Pileggi, L., & Kolter, J. Z. (2021). Adversarially robust learning for security-constrained optimal power flow. NeurIPS. [Pdf]

Donti, P. L., Roderick, M., Fazlyab, M., & Kolter, J. Z. (2021). Enforcing robust control guarantees within neural network policies. ICLR. [Pdf]

Donti, P. L., Rolnick, D., & Kolter, J. Z. (2021). DC3: A learning method for optimization with hard constraints. ICLR. [Pdf]

Eiter, T., Geibinger, T., Musliu, N., Oetsch, J., Skočovský, P. & Stepanova, D. (2021). Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling. KR. PDF

Fayyaz, M., Bahrami Rad, E., Diba, A., Noroozi, M., Adeli, E., Van Gool, L., & Gall, J. (2021). 3D CNNs with Adaptive Temporal Feature Resolutions. CVPR. [Pdf]

Franke, J. K. H., Koehler, G., Biedenkapp, A., & Hutter, F. (2021). Sample-Efficient Automated Deep Reinforcement Learning. ICLR. [Pdf]

Garcia, V., Hoogeboom, E., Fuchs, F., Posner, I., & Welling, M. (2021). E(n) Equivariant Normalizing Flows. NeurIPS. [Pdf]

Grünewald, S., Piccirilli, P., & Friedrich, A. (2021). Coordinate Constructions in Enhanced Universal Dependencies: Analysis and Computational Modeling. EACL. [Pdf]

Gurumurthy, S., Bai, S., Manchester, Z., & Kolter, J. Z. (2021). Joint inference and input optimization in equilibrium networks. NeurIPS. [Pdf]

Haussmann, M., Gerwinn, S., Look, A., Rakitsch, B., & Kandemir, M. (2021). Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS. [Pdf]

Hedderich, M. A., Lange, L., Adel, H., Stroetgen, J., & Klakow, D. (2021). A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios. NAACL. [Pdf]

Huang, Y., Zhang, H., Shi, Y., Kolter, J. Z., & Anandkumar, A. (2021). Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds. NeurIPS. [Pdf]

Huang, Z., Bai, S., & Kolter, J. Z. (2021). (Implicit)^2: Implicit Layers for Implicit Representations. NeurIPS. [Pdf]

Isele, S. T., Schilling, M. P., Klein, F. E., Saralajew, S., & Zoellner, J. M. (2021). Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets. Vehits. [Pdf]

Jain, N., Tran, T. K., Gad-Elrab, M. H. & Stepanova, D. (2021). Improving Knowledge Graph Embeddings with Ontological Reasoning. ISWC. PDF

Keller, T. A., & Welling, M. (2021). Topographic VAEs learn equivariant capsules. NeurIPS.

Kumar Mummadi, C., Subramaniam, R., Hutmacher, R., Vitay, J., Fischer, V., & Metzen, J. H. (2021). Does enhanced shape bias improve neural network robustness to common corruptions? ICLR. [Pdf]

Metzen, J. H., & Yatsura, M. (2021). Efficient Certified Defenses Against Patch Attacks on Image Classifiers. ICLR. [Pdf]

Oren, J., Ross, C., Lefarov, M., Richter, F., Taitler, A., Feldman, Z., Daniel, C., & Di Castro, D. (2021). SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems. SOCS. [Pdf]

Ott, K., Katiyar, P., Hennig, P., & Tiemann, M. (2021). ResNet After All: Neural ODEs and Their Numerical Solution. ICLR. [Pdf]

Otto, F., Becker, P., Ngo, V. A., Ziesche, H. C., & Neumann, G. (2021). Differentiable Trust Region Layers for Deep Reinforcement Learning. ICLR. [Pdf]

Pabbaraju, C., Winston, E., & Kolter, J. Z. (2021). Estimating Lipschitz constants of monotone deep equilibrium models. ICLR. [Pdf]

Patel, K., Beluch, W., Rambach, K., Cozma, A., Pfeiffer, M., & Yang, B. (2021). Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra. IEEE Radar Conference. PDF

Patel, K., Beluch, W., Yang, B., Pfeiffer, M., & Zhang, D. (2021). Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning. ICLR. [Pdf]

Qiu, C., Pfrommer, T., Kloft, M., Mandt, S., & Rudolph, M. (2021). Neural Transformation Learning for Deep Anomaly Detection Beyond Images. ICML. [Pdf]

Rice, L., Bair, A., Zhang, H., & Kolter, J. Z. (2021). Evaluating the spectrum of classifier robustness. NeurIPS. [Pdf]

Rudenko, A., Palmieri, L., Doellinger, J., Lilienthal, A. J., & Arras, K. O. (2021). Learning Occupancy Priors of Human Motion From Semantic Maps of Urban Environments. IEEE Robotics and Automation Letters, 6(2), 3248–3255. [Pdf]

Saseendran, A., Skubch, K., Falkner, S., & Keuper, M. (2021). Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders. NeurIPS. [Pdf]

Schoenfeld, E., Sushko, V., Zhang, D., Gall, J., Schiele, B., & Khoreva, A. (2021). You Only Need Adversarial Supervision for Semantic Image Synthesis. ICLR. [Pdf]

Sokota, S., Ho, C., Ahmad, Z. F., & Kolter, J. Z. (2021). Monte Carlo Tree Search With Iteratively Refining State Abstractions. NeurIPS. [Pdf]

Spector, O., & Di Castro, D. (2021). InsertionNet - A Scalable Solution for Insertion. RA-L. [Pdf]

Trockman, A., & Kolter, J. Z. (2021). Orthogonalizing Convolutional Layers with the Cayley Transform. ICLR. [Pdf]

Volpp, M., Fluerenbrock, F., Grossberger, L., Daniel, C., & Neumann, G. (2021). Bayesian Context Aggregation for Neural Processes. ICLR. [Pdf]

Wang, S., Zhang, H., Xu, K., Lin, X., Jana, S., Hsieh, C.-J., & Kolter, J. Z. (2021). Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification. NeurIPS.[Pdf]

Wong, E., & Kolter, J. Z. (2021). Learning perturbation sets for robust machine learning. ICLR. [Pdf]

Yatsura, M., Metzen, J. H., & Hein, M. (2021). Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks. NeurIPS. [Pdf]

Zaidi, S., Zela, A., Elsken, T., Holmes, C., Hutter, F., & Teh, Y. W. (2021). Neural Ensemble Search for Uncertainty Estimation and Dataset Shift. NeurIPS. [Pdf]

Zhai, R., Dan, C., Suggala, A., Kolter, J. Z., & Ravikumar, P. K. (2021). Boosted CVaR Classification. NeurIPS. [Pdf]

Zhao, J., Dong, Y., Ding, M., Kharlamov, E., & Tang, J. (2021). Adaptive Diffusion in Graph Neural Networks. NeurIPS. [Pdf]

Bai, S., Koltun, V., & Kolter, Z. (2020). Multiscale Deep Equilibrium Models. NeurIPS. [Pdf]

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]

Curi, S., Berkenkamp, F., & Krause, A. (2020). Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning. NeurIPS. [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]

Fathony, R., & Kolter, J. Z. (2020). AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning. AISTATS. [Pdf]

Feng, W., Zhang, J., Dong, Y., Han, Y., Luan, H., Xu, Q., Yang, Q., Kharlamov, E., & Tang, J. (2020). Graph Random Neural Networks for Semi-Supervised Learning on Graphs. NeurIPS. [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., & Lange L. (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]

Fuchs, F. B., Worrall, D. E., Fischer, V., & Welling, M. (2020). SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. NeurIPS. [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]

Hoogeboom, E., Garcia, V., Tomczak, J., & Welling, M. (2020). The Convolution Exponential and Generalized Sylvester Flows. NeurIPS. [Pdf]

Jaquier, N., & Rozo, L. (2020). High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds. NeurIPS. [Pdf]

Jaquier, N., Rozo, L., Caldwell, D. G., & Calinon, S. (2020). Geometry-aware manipulability learning, tracking, and transfer. The International Journal of Robotics and Research, 40(2-3), 624–650. [Pdf]

Kalayci, E. G., Gonzalez, I. G., Loesch, F., Xiao, G., Mehdi, A. U., Kharlamov, E., & Calvanese, D. (2020). Semantic Integration of Bosch Manufacturing Data Using Virtual Knowledge Graphs. ISWC. [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]

Lindinger, J., Reeb, D., Lippert, C., & Rakitsch, B. (2020). Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. NeurIPS. [Pdf]

Ling, C. K., Fang, F., & Kolter, Z. (2020). Deep Archimedean Copulas. NeurIPS. [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]

McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J., & Schütze, H. (2020). Placing Language in an Integrated Understanding System: Next Steps toward Human-Level Performance in Neural Language Models. PNAS. [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]

Nielsen, D., Jaini, P., Hoogeboom, E., & Welling, M. (2020). SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. NeurIPS. [Pdf]

Pabbaraju, C., Wang, P.-W., & Kolter, Z. (2020). Efficient semidefinite-programming-based inference for binary and multi-class MRFs. NeurIPS. [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]

Salman, H., Sun, M., Yang, G., Kapoor, A., & Kolter, Z. (2020). Denoised Smoothing: A Provable Defense for Pretrained Classifiers. NeurIPS. [Pdf]

Schirrmeister, R. T., Zhou, Y., Ball, T., & Zhang, D. (2020). Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features. NeurIPS. [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]

Schuff, H., Adel, H., & Vu N. T. (2020). F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering. EMNLP. [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]

Svetashova, Y., Zhou, B., Pychynski, T., Schmid, S., Sure-Vetter, Y., Mikut, R., & Kharlamov, E. (2020). Ontology-Enhanced Machine Learning Pipeline: a Bosch Use Case of Welding Quality Monitoring. ISWC. [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]

Van der Pol, E., Worrall, D., van Hoof, H., Oliehoek, F., & Welling, M. (2020). MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. NeurIPS. [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]

Wang, P.-W., & Kolter, Z. (2020). Community detection using fast low-cardinality semidefinite programming. NeurIPS. [Pdf]

Winston, E., & Kolter, Z. (2020). Monotone operator equilibrium networks. NeurIPS. [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]

Zimmer, C., & Yaesoubi, R. (2020). Influenza forecasting framework based on Gaussian processes. ICML. [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.

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., & Schober, M. (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]

Reeb, D., Doerr, A., Gerwinn, S. & Rakitsch, B. (2018). Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds. NeurIPS.[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]

Zimmer, C., Meister, M. & Nguyen-Tuong, D. (2018). Safe Active Learning for Time-Series Modeling with Gaussian Processes. NeurIPS. [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]

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]

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]

Wagner, J., Fischer, V., Herman, M., & Behnke, S. (2017). Learning Semantic Prediction using Pretrained Deep Feedforward Networks. ESANN. [Pdf]

Zhang, S., Bahrampour, S., & Ramakrishnan, N. (2017). Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. IEEE Xplore. [Pdf]

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]

Dhar, S., Naveen, N., Cherkassky, V., & Shah, M. (2016). Universum Learning for Multiclass SVM. SVM. [Pdf]

Hartmann, B., Kloppenburg, E., Heuser, P., & Diener, R. (2016). Online-Methods for Engine Test Bed Measurements Considering Engine Limits. ISSAM. [Pdf]

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

Heit, J., Liu, J., & Shah, M. (2016b). An architecture for the deployment of statistical models for the big data era. IEEE. [Pdf]

Herman, M., Gindele, T., Wagner, J., Schmitt, F., & Burgard, W. (2016a). Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics. AISTATS. [Pdf]

Herman, M., Gindele, T., Wagner, J., Schmitt, F., & Burgard, W. (2016b). Simultaneous Estimation of Rewards and Dynamics from Noisy Expert Demonstrations. ESANN. [Pdf]

Koerts, F., Bürger, M., van der Schaft, A., & De Persis, C. (2016). Stability Analysis of Networked Systems Containing Damped and Undamped Nodes. ACC. [Pdf]

Metzen, J. H. (2016). Minimum Regret Search for Single- and Multi-Task Optimization. ICML. [Pdf]

Peters, J., Lee, D. D., Kober, J., Nguyen-Tuong, D., Bagnell, J. A., & Schaal, S. (2016). Robot Learning (Handbook ed.). Springer, Cham. [Pdf]

Schiegg, M., Diego, F., & Hamprecht, F. A. (2016). Learning Diverse Models: The Coulomb Structured Support Vector Machine. ECCV. [Pdf]

Schillinger, M., Ortelt, B., Hartman, B., Schreiter, J., Meister, M., & Nelles, O. (2016). Safe Active Learning of High Pressure Fuel Supply Systems. EUROSIM. [Pdf]

Schillinger, P., Bürger, M., & Dimarogonas, D. V. (2016). Decomposition of Finite LTL Specifications for Efficient Multi-Agent Planning. DARS. [Pdf]

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]

Schreiter, J., Nguyen-Tuong, D., & Toussaint, M. (2016). Efficient Sparsification for Gaussian Process Regression. Neuro Comp. [Pdf]

Vinogradska, J., Bischoff, B., Nguyen-Tuong, D., Schmidt, H., Romer, A., & Peters, J. (2016). Stability of Controllers for Gaussian Process Forward Models. ICML. [Pdf]

Wagner, J., Fischer, V., Herman, M., & Behnke, S. (2016). Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks. ESANN. [Pdf]

Dhar, S., Ramakrishnan, C. Y. N., & Shah, M. (2015). ADMM based Scalable Machine Learning on Spark. ADMM. [Pdf]

Herman, M., Fischer, V., Gindele, T., & Burgard, W. (2015). Inverse Reinforcement Learning of Behavioral Models for Online-Adapting Navigation Strategies. ICRA. [Pdf]

Schreiter, J., Englert, P., Nguyen-Tuong, D., & Toussaint, M. (2015). Sparse Gaussian Process Regression for Compliant, Real-time Robot Control. ICRA. [Pdf]

Schreiter, J., Nguyen-Tuong, D., Eberts, M., Bischoff, B., Markert, H., & Toussaint, M. (2015). Safe Exploration for Active Learning with Gaussian Processes. ECML. [Pdf]

Bischoff, B., Nguyen-Tuong, D., Koller, T., Markert, H., & Knoll, A. (2014). Learning Throttle Valve Control Using Policy Search. ECML. [Pdf]

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]

Tietze, N., Konigorski, U., Fleck, C., & Nguyen-Tuong, D. (2014). Model-based Calibration of Engine Controller Using Automated Transient Design of Experiment. ISSAM. [Pdf]

Bischoff, B., Markert, H., Knoll, A., & Nguyen-Tuong, D. (2013). Solving the 15-Puzzle Game Using Local Value-Iteration. SCAI. [Pdf]

Bischoff, B., Nguyen-Tuong, D., Markert, H., & Knoll, A. (2013). Learning Control Under Uncertainty: A Probabilistic Value-Iteration Approach. ESANN. [Pdf]

Bischoff, B., Nguyen-Tuong, D., Lee, I.-H., Streichert, F., & Knoll, A. (2013). Hierarchical Reinforcement Learning for Robot Navigation. ESANN. [Pdf]

Bischoff, B., Nguyen-Tuong, D., Streichert, F., Ewert, M., & Knoll, A. (2012). Fusing Vision and Odometry for Accurate Indoor Robot Localization. ICARCV. [Pdf]

ERROR: No link has been specified!, & Peters, J. (2012). Online Kernel-Based Learning for Task-Space Tracking Robot Control. TransNN. [Pdf]