Skip to main content
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.

2024
2024
Akinwande, V., Jiang, Y., Sam, D. & Kolter, J. Z. (2024). Understanding prompt engineering may not require rethinking generalization. ICLR. PDF
2024
Baek, C., Kolter, J. Z. & Raghunathan, A. Why is SAM Robust to Label Noise? ICLR. PDF
2024
Beik-Mohammedi, H., Hauberg, S., Arvanitidis, G., Figueroa, N., Neumann, G. & Rozo, L. Neural Contractive Dynamical Systems. PDF
2024
Ensinger, K., Tagliapietra, N., Ziesche, S. & Trimpe, S. Exact Inference for Continuous-Time Gaussian Process Dynamics. PDF
2024
Ensinger, K., Ziesche, S. & Trimpe, S. Learning Hybrid Dynamics Models with Simulator-Informed Latent States. PDF
2024
He, Y., Murata, N., Lai, C., Takida, Y., Uesaka, T., Kim, D., Liao, W., Mitsufuji, Y., Kolter, J., Z., Salakhutdinov R. & Ermon, S. Manifold Preserving Guided Diffusion. ICLR. PDF
2024
Jiang, Y., Baek, C. & Kolter, J. Z. On the Joint Interaction of Models, Data, and Features. ICLR. PDF
2024
Li, Y., Keuper, M., Zhang, D. & Khoreva, A. Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive. ICLR. PDF
2024
Maini, P., Goyal, S., Lipton, Z., Kolter, J. Z. & Raghunathan, A. T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. ICLR. PDF
2024
Sokota, S., Farina, G., Wu, D., Hu, W., Wang, K., Kolter, J. Z. & Brown, N. The Update Equivalence Framework for Decision-Time Planning. ICLR. PDF
2024
Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A Simple and Effective Pruning Approach for Large Language Models. ICLR. PDF
2024
Tebbe, J., Zimmer, C., Steland, A., Lange-Hegermann, M. & Mies, F. Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning. AISTATS.
2024
Zhai, R., Liu, B., Risteski, A., Kolter, J. Z. & Ravikumar, P. Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression. ICLR. PDF
2023
2023
Andresel, M., Kien, T., Domokos, C., Minervini, P. & Stepanova, D. (2023). Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs. CIKM
2023
Beik-Mohammadi, H., Hauberg, S., Arvanitidis, G., Neumann, G. & Rozo, L. (2023). Reactive Motion Generation on Learned Riemannian Manifolds. IJRR. PDF
2023
Bitzer, M., Meister, M. & Zimmer, C. (2023). Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels. UAI.
2023
Bitzer, M., Meister, M. & Zimmer, C. (2023). Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems. AISTATS.
2023
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
2023
Carlini, N., Kolter, J. Z., Tramer, F., Dvijotham, K. D., Rice, L. & Sun, M. (2023). (Certified!!) Adversarial Robustness for Free!. ICLR. PDF
2023
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.
2023
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.
2023
Chubanov, S. (2023). On the complexity of PAC learning in Hilbert spaces. AAAI.
2023
Cui, P., Zhang, D., Deng, Z., Dong, Y. & Zhu, J. (2023). Learning Sample Difficulty from Pre-trained Models for Reliable Prediction. NeurIPS. PDF
2023
Cohen, L., Mansour, Y. & Moshkovitz, M. (2023).Finding Safe Zones of Markov Decision Processes Policies. NeurIPS. PDF
2023
De Avila Belbute-Peres, F. & Kolter, J. Z. (2023). Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth. ICLR. PDF
2023
Ensinger, K., Ziesche, S., Rakitsch, B., Tiemann, M. & Trimpe, S. (2023). Combining Slow and Fast: Complementary Filtering for Dynamics Learning. AAAI.
2023
Flynn, H., Reeb, D., Kandemir, M. & Peters, J. (2023). PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. TPAMI. PDF
2023
Flynn, H., Reeb, D., Kandemir, M. & Peters, J. (2023). Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. NeurIPS. PDF
2023
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
2023
Gruner, T., Belousov, B., Muratore, F., Palenicek, D. & Peters, J. (2023). Pseudo-Likelihood Inference. NeurIPS.
2023
He, Y., Nayyeri, M., Xiong, B., Zhu, Y., Kharlamov, E. & Staab, S. (2023). Can Pattern Learning Enhance Complex Logical Query Answering?. ISWC.
2023
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.
2023
Huang, H., Geiger, A. & Zhang, D. (2023). GOOD: Exploring geometric cues for detecting objects in an open world. ICLR. PDF
2023
Hung, C., Willmott, D. & Kolter, J. Z.(2023). TADA - Efficient Task-Agnostic Domain Adaptation for Transformers. ACL.
2023
Ismaeil, Y., Stepanova, D., Kien, T. & Bloeckeel, H. (2023). Feabi: A Feature Selection-based Framework for Interpreting Knowledge Graph Embeddings. ISWC.
2023
Jazbec, M., Allingham, J., Zhang, D. & Nalisnick, E. (2023): Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity. NeurIPs. PDF
2023
Klironomos, A., Zhou, B., Tan, Z., Zheng, Z., Mohamed, G., Paulheim, H. & Kharlamov, E. (2023). ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics. ESWC.
2023
Lange, L., Stroetgen, J., Adel, H. & Klakow, D. (2023). Multilingual Normalization of Temporal Expressions with Masked Language Models. EACL.
2023
Li, A., Qiu, C., Kloft, M., Smyth, P., Mandt, S. & Rudolph, M. (2023). Deep anomaly detection under labeling budget constraints. ICML. PDF.
2023
Li, A., Qiu, C., Kloft, M., Smyth, P., Mandt, S. & Rudolph, M. (2023). Zero-Shot Batch-Level Anomaly Detection. NeurIPS. PDF.
2023
Li, Y., Zhang, D., Keuper, M. & Khoreva, A. (2023). Intra-Source Style Augmentation for Improved Domain Generalization.WACV. PDF
2023
Li, Y., Zhang, D., Keuper, M. & Khoreva, A. (2023). Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization. IJCV.
2023
Luis, C. E., Bottero, A. G., Vinogradska, J., Berkenkamp, F., & Peters, J. (2023). Model-Base Uncertainty in Value Functions. AISTATS.
2023
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.
2023
Metzen, J., Hutmacher, R., Hua, N., Boreiko, V., & Zhang, D.(2023). Identification of Systematic Errors of Image Classifiers on Rare Subgroups. ICCV.
2023
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.
2023
Müller, J., Radev, S., Schmier, R., Draxler, F., Rother, C. & Koethe, U. (2023). Finding Competence Regions in Domain Generalization. TMLR. PDF
2023
Nurlanov, Z., Schmidt, F. R. & Bernard, F. (2023). Universe Points Representation Learning for Partial Multi-Graph Matching. AAAI. PDF
2023
Ott, K., Tiemann, M, Hennig, P. & Briol, F. (2023). Bayesian Numerical Integration with Neural Networks. UAI. PDF
2023
Ott, K., Betz, P., Stepanova, D., Gad-Elrab, M., Meilicke, C. & Stuckenschmidt, H. (2023). Rule-based Knowledge Graph Completion with Canonical Models. CIKM.
2023
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
2023
Rauch, C., Long, R., Ivan, V. & Vijayakumar, S. (2023). Sparse-Dense Motion Modelling and Tracking for Manipulation Without Prior Object Models. ICRA.
2023
Reeb, D., Patel, K., Barsim, K., Schiegg, M. & Gerwinn, S. (2023). Validation of Composite Systems by Disrepancy Propagation. UAI. PDF
2023
Schmier, R., Köthe, U. & Straehle, C. (2023). Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data. PDF
2023
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
2023
Schroeder de Witt, C., Sokota, S., Kolter, J. Z., Foerster, J. N. & Strohmeier, M. (2023). Perfectly Secure Steganography Using Minimum Entropy Coupling. ICLR. PDF
2023
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
2023
Shala, G., Elsken, T., Hutter, F. & Grabocka, J. (2023). Transfer NAS with Meta-learned Bayesian Surrogates. ICLR. PDF
2023
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
2023
Song, Y., Keller, T., Sebe, N. & Welling, M. (2023). Flow Factorized Representation Learning. NeurIPS. PDF
2023
Sushko, V., Zhang, D. , Gall, J. & Khoreva, A. (2023). One-Shot Synthesis of Images and Segmentation Masks. WACV. PDF
2023
Sushko, V., Wang, R. & Gall, J. (2023). Smoothness Similarity Regularization for Few-Shot GAN Adaptation. ICCV
2023
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.
2023
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.
2023
Taranovic, A., Kupcsik, A. G., Freymuth, N. & Neumann, G. (2023). Adversarial Imitation Learning with Preferences. ICLR. PDF
2023
Tatarchenko, M. & Rambach, K. (2023). Histogram-based Deep Learning for Automotive Radar. PDF
2023
Trockman, A., Willmott, D.& Kolter, J. Z. (2023). Understanding the Covariance Structure of Convolutional Filters. ICLR. PDF
2023
Veseli, B., Singhania, S., Razniewski, S. & Weikum, G. (2023). Evaluating Language Models for Knowledge Base Completion. ESWC. PDF
2023
Volpp, M., Dahlinger, P., Becker, P., Daniel, C.& Neuma, G. (2023). Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference. ICLR. PDF
2023
Wang, M., Adel, H., Lange, L., Strötgen, J. & Schütze, H. (2023). GradSim: Gradient-Based Language Grouping for Effective Multilingual Training. EMNLP.
2023
Wang, X., Cheng, G., Pan, J., Kharlamov, E.& Qu, Y. (2023). BANDAR: Benchmarking Snippet Generation Algorithms for (RDF) Dataset Search. TKDE
2023
Yatsura, M., Sakmann, K., Hua, N. G., Hein, M. & Metzen, J. H. (2023). Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation. ICLR. PDF
2023
Zhai, R., Dan, C., Kolter, J. Z.& Ravikumar, P. K. (2023). Understanding Why Generalized Reweighting Does Not Improve Over ERM. ICLR. PDF
2023
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.
2023
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.
2023
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.
2023
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.
2023
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.
2023
Zhu, Y., Potyka, N., Xiong, B., Tran, T., Nayyeri, M., Staab, S. & Kharlamov, E. (2023). Towards Statistical Reasoning with Ontology Embeddings. ISWC.
2023
Ziesche, H. & Rozo, L. (2023). Wasserstein Gradient Flows for Optimizing Gaussian Mixture Policies. NeurIPS.
2022
2022
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.
2022
Adrian, D., Kupcsik, A., Spies, M., & Neumann H. (2022). Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation. ICRA.
2022
Arnaout, H., Tran, T.-K., Stepanova, D., Gad-Elrab, M.H., Razniewski, S., & Weikum G. (2022). Utilizing Language Models for Knowledge Graph Repair. WWW.
2022
Baek, C., Jiang, Y., Raghunathan, A., & Kolter, J. Z. (2022). Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift. NeurIPS.
2022
Bai, S., Koltun, V., & Kolter, J. Z. (2022). Neural Deep Equilibrium Solvers. ICLR.
2022
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
2022
Bitzer, M., Meister, M., & Zimmer, C. (2022). Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport. NeurIPS.
2022
Bottero, A. G., Luis, C. E., Vinogradska, J., Berkenkamp, F. & Peters, J. (2022). Information Theoretic Safe Exploration with Gaussian Processes. NeurIPS.
2022
Di Castro, S., Mannot, S., & Di Castro, D. (2022). Analysis of Stochastic Processes through Replay Buffers. ICML.
2022
Duffhauss, F., Ngo, A. V., Ziesche, H., & Neumann, G. (2022). FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion. ECCV.
2022
Eiter, T., Geibinger, T., Higuera, N., Musliu, N., Oetsch, J., & Stepanova, D. (2022). Large-Neighbourhood Search for Optimisation in Answer-Set Solving. AAAI.
2022
Eiter, T., Geibinger, T., Musliu, N., Oetsch, N. J., & Stepanova, D. (2022). Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling. AAAI.
2022
Eiter, T., Geibinger, T., Musliu, N., Oetsch, N. J., & Stepanova D. (2022). ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser. KR.
2022
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.
2022
Ferreira, F., Nierhoff, T., Sälinger, A., & Hutter, F. (2022). Learning synthetic environments and reward networks for reinforcement learning. ICLR. PDF
2022
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., & Hutter, F. (2022). Auto-sklearn 2.0: Hands-free automl via meta-learning. JMLR. PDF
2022
Freymuth, N., Schreiber, N., Taranovic, A., Becker, P., & Neumann, G. (2022). Inferring Versatile Plans from Demonstrations by Matching Geometric Features. CoRL. PDF
2022
Fröhlich, L., Lefarov, M., Zeilinger, M., & Berkenkamp, F. (2022). On-Policy Model Errors in Reinforcement Learning. ICLR. PDF
2022
Gao, N., Ziesche, H., Vien, N.A., Volpp, M., & Neumann G. (2022). What Matters For Meta-Learning Vision Regression Tasks?. CVPR.
2022
Geiger, P., & Straehle, C.-N. (2022). Fail-Safe Adversarial Generative Imitation Learning. TMLR. PDF
2022
Goyal, S., Sun, M., Raghunathan, A., & Kolter, J. Z. (2022). Test Time Adaptation via Conjugate Pseudo-labels. NeurIPS. PDF
2022
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
2022
Ho, V. T., Stepanova, D., Milchevski, D., Stroetgen, J., & Weikum, G. (2022). Enhancing Knowledge Bases with Quantity Facts. WWW.
2022
Hoffmann, D. T., Behrmann, N., Gall, J., Brox, T., & Noroozi, M. (2022). Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives. AAAI.
2022
Jiang, Y., Liu, E. Z., Eysenbach, B., Kolter, J. Z., & Finn, C. (2022). Learning Options via Compression. NeurIPS.
2022
Jiang, Y., Nagarajan, V., Baek, C., & Kolter, J. Z. (2022). Assessing Generalization of SGD via Disagreement. ICLR.
2022
Kosman, E., & Di Castro, D. (2022). GraphVid: It Only Takes a Few Nodes to Understand a Video. ECCV. PDF
2022
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
2022
Levinkov, E., Kardoost, A., Andres, B., & Keuper M. (2022). Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation. TPAMI.
2022
Li, C. Y., Rakitsch, B., & Zimmer, C. (2022). Safe Active Learning for Multi-Output Gaussian Processes. AISTATS.
2022
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.
2022
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
2022
Lindinger, J., Rakitsch, B., & Lippert, C. (2022). Laplace Approximated Gaussian Process State-Space Models. UAI. PDF
2022
Liu, X., Hong, H., Wang, X., Chen, Z., Kharlamov, E., Dong, Y., & Tang, J. (2022). SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs. WWW.
2022
Look, A., Rakitsch, B., Kandemir, M., & Peters, J. (2022). A Deterministic Approximation to Neural SDEs. PAMI.
2022
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.
2022
Maini, P., Garg, S., Lipton, Z. C., & Kolter, J. Z. (2022). Characterizing Datapoints via Second-Split Forgetting. NeurIPS.
2022
Manek, G., & Kolter, J. Z. (2022). The Pitfalls of Regularization in Off-Policy TD Learning. NeurIPS.
2022
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
2022
Moskalev, A., Sosnovik, I., Fischer, V., & Smeulders, A. (2022). Contrasting quadratic assignments for set-based representation learning. ECCV. PDF
2022
Müller, S., Hollmann, N., Arango, S. P., Grabocka, J., & Hutter, F. (2022). Transformers can do bayesian inference. ICLR. PDF
2022
Nonnenmacher, M., Pfeil, T., Steinwart, I., & Reeb, D. (2022). SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. ICLR.
2022
Nonnenmacher, M., Oldenburg, L., Steinwart, I., & Reeb, D. (2022). Utilizing Expert Features for Contrastive Learning of Time-Series Representations. ICML.
2022
Öztürk, E., Ferreira, F., Jomaa, H., Schmidt-Thieme, L., Grabocka, J., & Hutter, F. (2022). Zero-shot automl with pretrained models. ICML. PDF
2022
Otto, F., Celik, O., Zhou, H., Ziesche, H., Ngo, A. V., & Neumann, G. (2022). Deep Black-Box Reinforcement Learning with Movement Primitives. CoRL. PDF
2022
Pokle, A., Geng, Z., & Kolter, J. Z. (2022). Deep Equilibrium Approaches to Diffusion Models. NeurIPS.
2022
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.
2022
Pujari, S., Mantiuk, F., Giereth, M., Stroetgen, J., & Friedrich, A. (2022). Evaluating Neural Multi-Field Document Representations for Patent Classification. BIR.
2022
Qiu, C., Kloft, M., Mandt, S., & Rudolph, M. (2022). Raising the bar in graph-level anomaly detection. IJCAI.
2022
Qiu, C., Li, A., Kloft, M., Rudolph, M., & Mandt, S. (2022). Latent Outlier Exposure for Anomaly Detection with Contaminated Data. ICML. PDF
2022
Saseendran, A., Skubch, K., & Keuper, M. (2022). Trading off Image Quality for Robustness is not Necessary with Deterministic Autoencoders. NeurIPS.
2022
Schirmer, M., Eltayeb, M., Lessmann, S., & Rudolph, M. (2022). Modeling Irregular Time Series with Continuous Recurrent Units. ICML. PDF
2022
Sepliarskaia, A., Moskalev, A., Sosnovik, I., & Smeulders, A. (2022). LieGG: Studying learned Lie group generators. NeurIPS. PDF
2022
Shi, Z., Wang, Y., Zhang, H., Kolter, J. Z., & Hsieh, C. (2022). Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. NeurIPS.
2022
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.
2022
Sushko, V., Schoenfeld, E., Zhang, D., Gall, J., Schiele, B., & Khoreva, A. (2022). OASIS: Only Adversarial Supervision for Semantic Image Synthesis. IJCV.
2022
Tighineanu, P., Skubch, K., Baireuther, P., Reiss, A., Berkenkamp, F., & Vinogradska, J. (2022). Transfer Learning with Gaussian Processes for Bayesian Optimization. AISTATS.
2022
Wei, C. & Kolter, J. Z. (2022). Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation. ICLR.
2022
Wöhlke, J., Schmitt, F., & van Hoof, H. (2022). Value Refinement Network (VRN). IJCAI.
2022
Xiong, B., Potyka, N., Tran, T., Nayyeri, M., & Staab, S. (2022). Faithful Embeddings for EL++ Knowledge Bases. ISWC.
2022
Yildiz, C., Kandemir, M., & Rakitsch, B. (2022). Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs. NeurIPS.
2022
Youmna, I., Stepanova, D., Tran, T., Saranrittichai, P., Domokos, C., & Blockeel, H. (2022). Towards Neural Network Interpretability Using Commonsense Knowledge Graphs. ISWC.
2022
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
2022
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.
2022
Zhang, J., Beik-Mohammadi, H., & Rozo, L. (2022). Learning Riemannian Stable Dynamical Systems via Diffeomorphisms. CoRL. PDF
2021
2021
Behrmann, N., Gall, J., & Noroozi, M. (2021). Unsupervised Video Representation Learning by Bidirectional Feature Prediction. WACV. [Pdf]
2021
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
2021
Chowdhury, S. N., Wickramarachchi, R., Gad-Elrab, M. H., Stepanova, D., & Henson, C. A. (2021). Towards Leveraging Commonsense Knowledge for Autonomous Driving. ISWC. PDF
2021
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]
2021
Di Castro, S. S., Mannor, S., & Di Castro, D. (2021). Sim and Real: Better Together. NeurIPS. [Pdf]
2021
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]
2021
Donti, P. L., Roderick, M., Fazlyab, M., & Kolter, J. Z. (2021). Enforcing robust control guarantees within neural network policies. ICLR. [Pdf]
2021
Donti, P. L., Rolnick, D., & Kolter, J. Z. (2021). DC3: A learning method for optimization with hard constraints. ICLR. [Pdf]
2021
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
2021
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]
2021
Franke, J. K. H., Koehler, G., Biedenkapp, A., & Hutter, F. (2021). Sample-Efficient Automated Deep Reinforcement Learning. ICLR. [Pdf]
2021
Garcia, V., Hoogeboom, E., Fuchs, F., Posner, I., & Welling, M. (2021). E(n) Equivariant Normalizing Flows. NeurIPS. [Pdf]
2021
Grünewald, S., Piccirilli, P., & Friedrich, A. (2021). Coordinate Constructions in Enhanced Universal Dependencies: Analysis and Computational Modeling. EACL. [Pdf]
2021
Gurumurthy, S., Bai, S., Manchester, Z., & Kolter, J. Z. (2021). Joint inference and input optimization in equilibrium networks. NeurIPS. [Pdf]
2021
Haussmann, M., Gerwinn, S., Look, A., Rakitsch, B., & Kandemir, M. (2021). Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS. [Pdf]
2021
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]
2021
Huang, Y., Zhang, H., Shi, Y., Kolter, J. Z., & Anandkumar, A. (2021). Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds. NeurIPS. [Pdf]
2021
Huang, Z., Bai, S., & Kolter, J. Z. (2021). (Implicit)^2: Implicit Layers for Implicit Representations. NeurIPS. [Pdf]
2021
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]
2021
Jain, N., Tran, T. K., Gad-Elrab, M. H. & Stepanova, D. (2021). Improving Knowledge Graph Embeddings with Ontological Reasoning. ISWC. PDF
2021
Keller, T. A., & Welling, M. (2021). Topographic VAEs learn equivariant capsules. NeurIPS.
2021
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]
2021
Metzen, J. H., & Yatsura, M. (2021). Efficient Certified Defenses Against Patch Attacks on Image Classifiers. ICLR. [Pdf]
2021
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]
2021
Ott, K., Katiyar, P., Hennig, P., & Tiemann, M. (2021). ResNet After All: Neural ODEs and Their Numerical Solution. ICLR. [Pdf]
2021
Otto, F., Becker, P., Ngo, V. A., Ziesche, H. C., & Neumann, G. (2021). Differentiable Trust Region Layers for Deep Reinforcement Learning. ICLR. [Pdf]
2021
Pabbaraju, C., Winston, E., & Kolter, J. Z. (2021). Estimating Lipschitz constants of monotone deep equilibrium models. ICLR. [Pdf]
2021
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
2021
Patel, K., Beluch, W., Yang, B., Pfeiffer, M., & Zhang, D. (2021). Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning. ICLR. [Pdf]
2021
Qiu, C., Pfrommer, T., Kloft, M., Mandt, S., & Rudolph, M. (2021). Neural Transformation Learning for Deep Anomaly Detection Beyond Images. ICML. [Pdf]
2021
Rice, L., Bair, A., Zhang, H., & Kolter, J. Z. (2021). Evaluating the spectrum of classifier robustness. NeurIPS. [Pdf]
2021
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]
2021
Saseendran, A., Skubch, K., Falkner, S., & Keuper, M. (2021). Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders. NeurIPS. [Pdf]
2021
Schoenfeld, E., Sushko, V., Zhang, D., Gall, J., Schiele, B., & Khoreva, A. (2021). You Only Need Adversarial Supervision for Semantic Image Synthesis. ICLR. [Pdf]
2021
Sokota, S., Ho, C., Ahmad, Z. F., & Kolter, J. Z. (2021). Monte Carlo Tree Search With Iteratively Refining State Abstractions. NeurIPS. [Pdf]
2021
Spector, O., & Di Castro, D. (2021). InsertionNet - A Scalable Solution for Insertion. RA-L. [Pdf]
2021
Trockman, A., & Kolter, J. Z. (2021). Orthogonalizing Convolutional Layers with the Cayley Transform. ICLR. [Pdf]
2021
Volpp, M., Fluerenbrock, F., Grossberger, L., Daniel, C., & Neumann, G. (2021). Bayesian Context Aggregation for Neural Processes. ICLR. [Pdf]
2021
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]
2021
Wong, E., & Kolter, J. Z. (2021). Learning perturbation sets for robust machine learning. ICLR. [Pdf]
2021
Yatsura, M., Metzen, J. H., & Hein, M. (2021). Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks. NeurIPS. [Pdf]
2021
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]
2021
Zhai, R., Dan, C., Suggala, A., Kolter, J. Z., & Ravikumar, P. K. (2021). Boosted CVaR Classification. NeurIPS. [Pdf]
2021
Zhao, J., Dong, Y., Ding, M., Kharlamov, E., & Tang, J. (2021). Adaptive Diffusion in Graph Neural Networks. NeurIPS. [Pdf]
2020
2020
Bai, S., Koltun, V., & Kolter, Z. (2020). Multiscale Deep Equilibrium Models. NeurIPS. [Pdf]
2020
Becker, P., Arenz, O., & Neumann, G. (2020). Expected Information Maximization: Using the I-Projection for Mixture Density Estimation. ICLR. [Pdf]
2020
Chubanov, S. (2020a). A polynomial algorithm for convex quadratic optimization subject to linear inequalities. Discrete Applied Mathematics, 275, 19–28. [Pdf]
2020
Chubanov, S. (2020b). A scaling algorithm for optimizing arbitrary functions over vertices of polytopes. Mathematical Programming, para. 1. [Pdf]
2020
Curi, S., Berkenkamp, F., & Krause, A. (2020). Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning. NeurIPS. [Pdf]
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]
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]
2020
Elsken, T., Staffler, B., Metzen, J. H., & Hutter, F. (2020). Meta-Learning of Neural Architectures for Few-Shot Learning. CVPR. [Pdf]
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]
2020
Fathony, R., & Kolter, J. Z. (2020). AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning. AISTATS. [Pdf]
2020
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]
2020
Forssell, H., Kharlamov, E., & Thorstensen, E. (2020). On Equivalence and Cores for Incomplete Databases in Open and Closed Worlds. ICDT. [Pdf]
2020
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]
2020
Fröhlich, L., Klenske, E., Vinogradska, J., Daniel, C., & Zeilinger, M. (2020). Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization. AISTATS. [Pdf]
2020
Fuchs, F. B., Worrall, D. E., Fischer, V., & Welling, M. (2020). SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. NeurIPS. [Pdf]
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]
2020
Gargiani, M., Zanelli, A., Dinh, Q. T., Diehl, M., & Hutter, F. (2020). Transferring Optimality Across Data Distributions via Homotopy Methods. ICLR. [Pdf]
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]
2020
Gulshad, S., & Smeulders, A. (2020). Explaining with Counter Visual Attributes and Examples. ICMR. [Pdf]
2020
Hewing, L., Arcari, E., Froehlich, L., & Zeilinger, M. (2020). On Simulation and Trajectory Prediction with Gaussian Process Dynamics. L4DC. [Pdf]
2020
Hoogeboom, E., Garcia, V., Tomczak, J., & Welling, M. (2020). The Convolution Exponential and Generalized Sylvester Flows. NeurIPS. [Pdf]
2020
Jaquier, N., & Rozo, L. (2020). High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds. NeurIPS. [Pdf]
2020
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]
2020
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]
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]
2020
Kipf, T., van der Pol, E., & Welling, M. (2020). Contrastive Learning of Structured World Models. ICLR. [Pdf]
2020
Klenske, E.D. Optimal test pooling for efficient PCR testing of SARS-CoV2. Ir J Med Sci (2020). [Pdf]
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]
2020
Lange, L., Adel, H., & Stroetgen, J. (2020a). Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain. ACL. [Pdf]
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]
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]
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]
2020
Li, S., Huang, Z., Cheng, G., Kharlamov, E., & Gunaratna, K. (2020). Enriching Documents with Compact, Representative, Relevant Knowledge Graphs. IJCAI. [Pdf]
2020
Lindinger, J., Reeb, D., Lippert, C., & Rakitsch, B. (2020). Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. NeurIPS. [Pdf]
2020
Ling, C. K., Fang, F., & Kolter, Z. (2020). Deep Archimedean Copulas. NeurIPS. [Pdf]
2020
Liu, Q., Chen, Y., Cheng, G., Kharlamov, E., Li, J., & Qu, Y. (2020). Entity Summarization with User Feedback. ESWC. [Pdf]
2020
Maini, P., Wong, E., & Kolter, Z. (2020). Adversarial Robustness Against the Union of Multiple Threat Models. ICML. [Pdf]
2020
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]
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]
2020
Nielsen, D., Jaini, P., Hoogeboom, E., & Welling, M. (2020). SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. NeurIPS. [Pdf]
2020
Pabbaraju, C., Wang, P.-W., & Kolter, Z. (2020). Efficient semidefinite-programming-based inference for binary and multi-class MRFs. NeurIPS. [Pdf]
2020
Rosenfeld, E., Winston, E., Ravikumar, P., & Kolter, Z. (2020). Certified Robustness to Label-Flipping Attacks via Randomized Smoothing. ICML. [Pdf]
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]
2020
Salman, H., Sun, M., Yang, G., Kapoor, A., & Kolter, Z. (2020). Denoised Smoothing: A Provable Defense for Pretrained Classifiers. NeurIPS. [Pdf]
2020
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]
2020
Schönfeld, E., Schiele, B., & Khoreva, A. (2020). A U-Net Based Discriminator for Generative Adversarial Networks. CVPR. [Pdf]
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]
2020
Schuff, H., Adel, H., & Vu N. T. (2020). F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering. EMNLP. [Pdf]
2020
Shi, Y., Cheng, G., & Kharlamov, E. (2020). Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings. WWW. [Pdf]
2020
Sosnovik, I., Szmaja, M., & Smeulders, A. (2020). Scale-Equivariant Steerable Networks. ICLR. [Pdf]
2020
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]
2020
Todescato, M., Carron, A., Carli, R., Pillonetto, G., & Schenato, L. (2020). Efficient spatio-temporal Gaussian regression via Kalman filtering. Automatica, 118, 109032. [Pdf]
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]
2020
Van der Pol, E., Kipf, T., Oliehoek, F., & Welling, M. (2020). Plannable Approximations to MDP Homomorphisms: Equivariance under actions. AAMAS. [Pdf]
2020
Van der Pol, E., Worrall, D., van Hoof, H., Oliehoek, F., & Welling, M. (2020). MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning. NeurIPS. [Pdf]
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]
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]
2020
Wang, P.-W., Stepanova, D., Domokos, C., & Kolter, J. Z. (2020). Differentiable learning of numerical rules in knowledge graphs. ICLR. [Pdf]
2020
Wang, P.-W., & Kolter, Z. (2020). Community detection using fast low-cardinality semidefinite programming. NeurIPS. [Pdf]
2020
Winston, E., & Kolter, Z. (2020). Monotone operator equilibrium networks. NeurIPS. [Pdf]
2020
Wöhlke, J., Schmitt, F., & van Hoof, H. (2020). A Performance-Based Start State Curriculum Framework for Reinforcement Learning. AAMAS. [Pdf]
2020
Wong, E., Rice, L., & Kolter, J. Z. (2020a). Fast is better than free: Revisiting adversarial training. ICLR. [Pdf]
2020
Wong, E., Rice, L., & Kolter, Z. (2020b). Overfitting in adversarially robust deep learning. ICML. [Pdf]
2020
Zela, A., Elsken, T., Saikia, T., Marrakchi, Y., Brox, T., & Hutter, F. (2020). Understanding and Robustifying Differentiable Architecture Search. ICLR. [Pdf]
2020
Zela, A., Siems, J., & Hutter, F. (2020). NAS-BENCH-1SHOT1: Benchmarkting and Dissecting One-Shot Neural Architecture Search. ICLR. [Pdf]
2020
Zimmer, C., Driess, D., Meister, M., & Nguyen-Tuong, D. (2020). Adaptive Discretization for Evaluation of Probabilistic Cost Functions. AISTATS. [Pdf]
2020
Zimmer, C., & Yaesoubi, R. (2020). Influenza forecasting framework based on Gaussian processes. ICML. [Pdf]
2019
2019
Agrawal, A., Amos, B., Barratt, S., Boyd, S., Diamond, S., & Kolter, J. Z. (2019). Differentiable Convex Optimization Layers. NeurIPS. [Pdf]
2019
Akrour, R., Pajarinen, J., & Neumann, G. (2019). Projections for Approximate Policy Iteration Algorithms. ICML. [Pdf]
2019
Angerbauer, K., Adel, H., & Vu, N. T. (2019). Automatic Compression of Subtitles with Neural Networks and its Effect on User Experiences. Interspeech. [Pdf]
2019
Arvanitis, G., Hauberg, S., Henning, P., & Schober, M. (2019). Fast and Robust Shortest Paths on Manifolds Learned from Data. AISTATS. [Pdf]
2019
Bai, S., Koltun, V., & Kolter, J. Z. (2019). Deep Equilibrium Models. NeurIPS. [Pdf]
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]
2019
Beggel, L., Pfeiffer, M., & Bischl, B. (2019). Robust Anomaly Detection in Images using Adversarial Autoencoders. ECML. [Pdf]
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]
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.
2019
Blaiotta, C. (2019). Learning Generative Socially Aware Models of Pedestrian Motion. IEEE Robotics and Automation Letters, 4(4), 3433–3440. [Pdf]
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]
2019
Cohen, J., Rosenfeld, E., & Kolter, Z. (2019). Certified Adversarial Robustness via Randomized Smoothing. ICML. [Pdf]
2019
Delhaisse, B., Rozo, L., & Caldwell, D. G. (2019). PyRoboLearn: A Python Framework for Robot Learning Practitioners. CoRL. [Pdf]
2019
Dikeoulias, I., Strötgen, J., & Razniewski, S. (2019). Epitaph or Breaking News? Analyzing and Predicting the Stability of Knowledge Base Properties. TempWeb. [Pdf]
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]
2019
Doerr, A., Volpp, M., Toussaint, M., Trimpe, S., & Daniel, C. (2019). Trajectory-Based Off-Policy Deep Reinforcement Learning. ICML. [Pdf]
2019
Elsken, T., Metzen, J. H., & Hutter, F. (2019a). Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution. ICLR. [Pdf]
2019
Elsken, T., Metzen, J. H., & Hutter, F. (2019b). Neural Architecture Search: A Survey. Journal of Machine Learning Research 20 (2, 1–21. [Pdf]
2019
Esteban, D., Rozo, L., & Caldwell, D. G. (2019). Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies. Iros. [Pdf]
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]
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]
2019
Gad-Elrab, M., Stepanova, D., Urbani, J., & Weikum, G. (2019a). ExFact: Explaining Facts over Knowledge Graphs and Text. WSDM. [Pdf]
2019
Gad-Elrab, M., Stepanova, D., Urbani, J., & Weikum, G. (2019b). Tracy: Tracing Facts over Knowledge Graphs and Text. WWW. [Pdf]
2019
Garcia, V., Akata, Z., & Welling, M. (2019). GRIN: Graphical Recurrent Inference Networks. NeurIPS.
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]
2019
Guo, M., & Bürger, M. (2019). Predictive Safety Network for Resource-constrained Multi-agent Systems. CoRL. [Pdf]
2019
Gupta, D., Berberich, K., Strötgen, J., & Zeinalipour-Yazti, D. (2019). Generating Semantic Aspects for Queries. ESWC. [Pdf]
2019
Haussmann, M., Hamprecht, F. A., & Kandemir, M. (2019a). Deep Active Learning with Adaptive Acquisition. IJCAI. [Pdf]
2019
Haussmann, M., Hamprecht, F. A., & Kandemir, M. (2019b). Sampling-Free Variational Inference of Bayesian Neural Nets with Variance Backpropagation. UAI. [Pdf]
2019
Hoogeboom, E., Peters, J., van den Berg, R., & Welling, M. (2019). Integer Discrete Flows and Lossless Compression. NeurIPS. [Pdf]
2019
Hoogeboom, E., Van den Berg, R., & Welling, M. (2019). Emerging Convolutions for Generative Normalizing Flows. ICML. [Pdf]
2019
Hoyer, L., Kesper, P., Khoreva, A., & Fischer, V. (2019). Short-Term Prediction and Multi-Camera Fusion on Semantic Grids. ICCV. [Pdf]
2019
Hoyer, L., Munoz, M., Katiyar, P., Khoreva, A., & Fischer, V. (2019). Grid Saliency for Context Explanations of Semantic Segmentation. NeurIPS. [Pdf]
2019
Huang, Y., Rozo, L., Silvério, J., & Caldwell, D. G. (2019). Kernelized movement primitives. The International Journal of Robotics Research, 38(7), 833–852. [Pdf]
2019
Huang, Y., Rozo, L., Silverio, J., & Caldwell, D. G. (2019). Non-parametric Imitation Learning of Robot Motor Skills. ICRA. [Pdf]
2019
Huang, Z., Li, S., Cheng, G., Kharlamov, E., & Qu, Y. (2019). Making Sense of News via Relationship Subgraphs. CIKM. [Pdf]
2019
Jaquier, N., Rozo, L., Calinon, S., & Bürger, M. (2019). Bayesian Optimization Meets Riemannian Manifolds in Robot Learning. CoRL. [Pdf]
2019
John, D., Heuveline, V., & Schober, M. (2019). GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver. ICML. [Pdf]
2019
Kemos, A., Adel, H., & Schütze, H. (2019). Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging. NAACL. [Pdf]
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]
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]
2019
Koch, M., Spies, M., & Bürger, M. (2019). Trust Regions for Safe Sampling-Based Model Predictive Control. ICRA. [Pdf]
2019
Köhler, J., Autenrieth, M., & Beluch, W. (2019). Uncertainty Based Detection and Relabeling of Noisy Image Labels. CVPR. [Pdf]
2019
Kusumoto, R., Palmieri, L., Spies, M., Csiszar, A., & Arras, K. O. (2019). Informed Information Theoretic Model Predictive Control. ICRA. [Pdf]
2019
Lange, L., Adel, H., & Stroetgen, J. (2019). NLNDE: Enhancing Neural Sequence Taggers with Attention and Noisy Channel for Robust Pharmacological Entity Detection. BioNLP. [Pdf]
2019
Lange, L., Adel, H., & Strötgen, J. (2019). NLNDE: The Neither-Language-Nor-Domain-Experts’ Way of Spanish Medical Document De-Identification. IberLEF. [Pdf]
2019
Lange, L., Alonso, O., & Strötgen, J. (2019). The Power of Temporal Features for Classifying News Articles. TempWeb. [Pdf]
2019
Lange, L., Hedderich, M. A., & Klakow, D. (2019). Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels. EMNLP. [Pdf]
2019
Li, B., Schmidt, F. R., & Kolter, Z. (2019). Adversarial camera stickers: A physical camera-based attack on deep learning systems. ICML. [Pdf]
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]
2019
Look, A., & Kandemir, M. (2019, December). Differential Bayesian Neural Nets [Workshop: Poster]. NeurIPS, Vancouver, Canada. [Pdf]
2019
Louizos, C., Shi, X., & Welling, M. (2019). The Functional Neural Process. NeurIPS. [Pdf]
2019
Mailis, T., Kotidis, Y., Nikolopoulos, V., Kharlamov, E., Horrocks, I., & Ioannidis, Y. (2019a). An Efficient Index for RDF Query Containment. SIGMOD. [Pdf]
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]
2019
Manek, G., & Kolter, J. Z. (2019). Learning Stable Deep Dynamics Models. NeurIPS. [Pdf]
2019
McHardy, R., Adel, H., & Klinger, R. (2019). Adversarial Training for Satire Detection: Controlling for Confounding Variables. NAACL. [Pdf]
2019
Mehdi, A., Kharlamov, E., Stepanova, D., Loesch, F., & Gonzales, I. G. (2019). Towards Semantic Integration of Bosch Manufacturing Data. ISCW (Industry Track). [Pdf]
2019
Mettes, P., van der Pol, E., & Snoek, C. (2019). Hyperspherical Prototype Networks. NeurIPS. [Pdf]
2019
Mummadi, C. K., Brox, T., & Metzen, J. H. (2019). Defending against universal perturbations with shared adversarial training. ICCV. [Pdf]
2019
Nagarajan, V., & Kolter, J. Z. (2019). Uniform convergence may be unable to explain generalization in deep learning. NeurIPS. [Pdf]
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]
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]
2019
Rozo, L. (2019). Interactive Trajectory Adaptation through Force-guided Bayesian Optimization. IROS. [Pdf]
2019
Savkovic, O., Kharlamov, E., & Lamparter, S. (2019). Validation of SHACL Constraints over KGs with OWL 2 Ontologies via Rewriting. ESWC. [Pdf]
2019
Schönfeld, E., Ebrahimi, S., Sinha, S., Darrall, T., & Akata, Z. (2019). Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders. CVPR. [Pdf]
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]
2019
Schwenkel, L., Guo, M., & Bürger, M. (2019). Optimizing Sequences of Probabilistic Manipulation Skills Learned from Demonstration. CoRL. [Pdf]
2019
Silverio, J., Huang, Y., Abu-dakka, F., Rozo, L., & Caldwell, D. G. (2019). Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives. IROS. [Pdf]
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]
2019
Trouleau, W., Etesami, J., Grossglauser, M., Kiyavash, M., & Thiran, P. (2019). Learning Hawkes Processes Under Synchronization Noise. ICML. [Pdf]
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]
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]
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]
2019
Wang, X., Cheng, G., & Kharlamov, E. (2019). Towards Multi-Facet Snippets for Dataset Search. PROFILES. [Pdf]
2019
Waniek, N. (2020). Transition Scale-Spaces: A Computational Theory for the Discretized Entorhinal Cortex. Neural Computation, 32(2), 330–394. [Pdf]
2019
Wong, E., Schmidt, F. R., & Kolter, Z. (2019). Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. ICML. [Pdf]
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]
2019
Zhang, D., & Khoreva, A. (2019). Progressive Augmentation of GANs. NeurIPS. [Pdf]
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]
2018
2018
Achterhold, J., Köhler, J. M., Schmeink, A., & Genewein, T. (2018). Variational Network Quantization. ICLR. [Pdf]
2018
Agarwal, P., Strötgen, J., del Corro, L., Hoffart, J., & Weikum, G. (2018). diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora. ACL. [Pdf]
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]
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]
2018
Domokos, C., Schmidt, F. R., & Cremers, D. (2018). MRF Optimization with Separable Convex Prior on Partially Ordered Labels. ECCV. [Pdf]
2018
Domokos, C., Schmidt, F. R., & Cremers, D. (2018). MRF Optimization with Separable Convex Prior on Partially Ordered Labels. ECCV. [Pdf]
2018
Estellers, V., Schmidt, F. R., & Cremers, D. (2018). Robust Fitting of Subdivision Surfaces for Smooth Shape Analysis. 3DV. [Pdf]
2018
Etesami, J., Habibnia, A., & Kiyavash, N. (2018). Econometric Modeling of Systemic Risk: A Time Series Approach. KDD. [Pdf]
2018
Fischer, V., Pfeil, T., & Köhler, J. (2018). The streaming rollout of deep networks - towards fully model-parallel execution. NIPS. [Pdf]
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]
2018
Pfeiffer, M., & Pfeil, T. (2018). Deep Learning With Spiking Neurons: Opportunities and Challenges. Frontiers in Neuroscience, 12, para. 1. [Pdf]
2018
Reeb, D., Doerr, A., Gerwinn, S. & Rakitsch, B. (2018). Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds. NeurIPS.[Pdf]
2018
Salehkaleybar, S., Etesami, J., Kiyavash, N., & Zhang, K. (2018). Learning Vector Autoregressive Models with Latent Processes. AAAI. [Pdf]
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]
2018
Waniek, N. (2018). Hexagonal Grid Fields Optimally Encode Transitions in Spatiotemporal Sequences. Neural Computation, 30(10), 2691–2725. [Pdf]
2018
Wong, E., Schmidt, F. R., Metzen, J. H., & Kolter, Z. (2018). Scaling provable adversarial defenses. NIPS. [Pdf]
2018
Zimmer, C., Meister, M. & Nguyen-Tuong, D. (2018). Safe Active Learning for Time-Series Modeling with Gaussian Processes. NeurIPS. [Pdf]
2017
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]
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]
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]
2017
Heit, J., Liu, J., & Shah, M. (2017). An Architecture for the Deployment of Statistical Models for the Big Data Era. IEEE. [Pdf]
2017
Meister, M., & Steinwart, I. (2017). Optimal Learning Rates for Localized SVMs. JMLR. [Pdf]
2017
Metzen, J. H., Genewein, T., Fischer, V., & Bischoff, B. (2017). On Detecting Adversarial Perturbations. ICLR. [Pdf]
2017
Metzen, J. H., Kumar, M. C., Brox, T., & Fischer, V. (2017). Universal Adversarial Perturbations Against Semantic Image Segmentation. ICCV. [Pdf]
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]
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]
2017
Wagner, J., Fischer, V., Herman, M., & Behnke, S. (2017). Learning Semantic Prediction using Pretrained Deep Feedforward Networks. ESANN. [Pdf]
2017
Zhang, S., Bahrampour, S., & Ramakrishnan, N. (2017). Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction. IEEE Xplore. [Pdf]
2016
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]
2016
Dhar, S., Naveen, N., Cherkassky, V., & Shah, M. (2016). Universum Learning for Multiclass SVM. SVM. [Pdf]
2016
Hartmann, B., Kloppenburg, E., Heuser, P., & Diener, R. (2016). Online-Methods for Engine Test Bed Measurements Considering Engine Limits. ISSAM. [Pdf]
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
2016
Heit, J., Liu, J., & Shah, M. (2016b). An architecture for the deployment of statistical models for the big data era. IEEE. [Pdf]
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]
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]
2016
Metzen, J. H. (2016). Minimum Regret Search for Single- and Multi-Task Optimization. ICML. [Pdf]
2016
Peters, J., Lee, D. D., Kober, J., Nguyen-Tuong, D., Bagnell, J. A., & Schaal, S. (2016). Robot Learning (Handbook ed.). Springer, Cham. [Pdf]
2016
Schiegg, M., Diego, F., & Hamprecht, F. A. (2016). Learning Diverse Models: The Coulomb Structured Support Vector Machine. ECCV. [Pdf]
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]
2016
Schillinger, P., Bürger, M., & Dimarogonas, D. V. (2016). Decomposition of Finite LTL Specifications for Efficient Multi-Agent Planning. DARS. [Pdf]
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]
2016
Schreiter, J., Nguyen-Tuong, D., & Toussaint, M. (2016). Efficient Sparsification for Gaussian Process Regression. Neuro Comp. [Pdf]
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]
2016
Wagner, J., Fischer, V., Herman, M., & Behnke, S. (2016). Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks. ESANN. [Pdf]
2015
2015
Dhar, S., Ramakrishnan, C. Y. N., & Shah, M. (2015). ADMM based Scalable Machine Learning on Spark. ADMM. [Pdf]
2015
Herman, M., Fischer, V., Gindele, T., & Burgard, W. (2015). Inverse Reinforcement Learning of Behavioral Models for Online-Adapting Navigation Strategies. ICRA. [Pdf]
2015
Schreiter, J., Englert, P., Nguyen-Tuong, D., & Toussaint, M. (2015). Sparse Gaussian Process Regression for Compliant, Real-time Robot Control. ICRA. [Pdf]
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]
2014
2014
Bischoff, B., Nguyen-Tuong, D., Koller, T., Markert, H., & Knoll, A. (2014). Learning Throttle Valve Control Using Policy Search. ECML. [Pdf]
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]
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]
2013
2013
Bischoff, B., Markert, H., Knoll, A., & Nguyen-Tuong, D. (2013). Solving the 15-Puzzle Game Using Local Value-Iteration. SCAI. [Pdf]
2013
Bischoff, B., Nguyen-Tuong, D., Markert, H., & Knoll, A. (2013). Learning Control Under Uncertainty: A Probabilistic Value-Iteration Approach. ESANN. [Pdf]
2013
Bischoff, B., Nguyen-Tuong, D., Lee, I.-H., Streichert, F., & Knoll, A. (2013). Hierarchical Reinforcement Learning for Robot Navigation. ESANN. [Pdf]
2012
2012
Bischoff, B., Nguyen-Tuong, D., Streichert, F., Ewert, M., & Knoll, A. (2012). Fusing Vision and Odometry for Accurate Indoor Robot Localization. ICARCV. [Pdf]
2012
ERROR: No link has been specified!, & Peters, J. (2012). Online Kernel-Based Learning for Task-Space Tracking Robot Control. TransNN. [Pdf]