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 |
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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. (2024). Why is SAM Robust to Label Noise? ICLR. PDF |
2024 Beik-Mohammedi, H., Hauberg, S., Arvanitidis, G., Figueroa, N., Neumann, G. & Rozo, L. (2024). Neural Contractive Dynamical Systems. PDF |
2024 Bini, M., Roth, K., Akata, Z. & Khoreva, A. (2024). ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections. ICML. PDF |
2024 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 |
2024 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. |
2024 Ensinger, K., Tagliapietra, N., Ziesche, S. & Trimpe, S. (2024). Exact Inference for Continuous-Time Gaussian Process Dynamics. PDF |
2024 Ensinger, K., Ziesche, S. & Trimpe, S. (2024). Learning Hybrid Dynamics Models with Simulator-Informed Latent States. PDF |
2024 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 |
2024 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 |
2024 Huang, H., Peng, S., Zhang, D. & Geiger, A. (2024). Renovating Names in Open-Vocabulary Segmentation Benchmarks. NeurIPs. PDF |
2024 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 |
2024 Jazbec, M., Forré, P., Mandt, S., Zhang, D. & Nalisnick, E. (2024). Early-Exit Neural Networks with Nested Prediction Sets. UAI. PDF |
2024 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 |
2024 Jiang, Y., Baek, C. & Kolter, J. Z. (2024). On the Joint Interaction of Models, Data, and Features. ICLR. PDF |
2024 Kälble, J., Wirges, S., Tatarchenko, M. & Ilg, E. (2024). Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory. PDF |
2024 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 |
2024 Li, Y., Keuper, M., Zhang, D. & Khoreva, A. (2024). Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive. ICLR. PDF |
2024 Mai, H. T., Chu, C. X. & Paulheim, H. (2024). Do LLMs Really Adapt to Domains? An Ontology Learning Perspective. ISWC. PDF |
2024 Maini, P., Goyal, S., Lipton, Z., Kolter, J. Z. & Raghunathan, A. (2024). T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. ICLR. PDF |
2024 Öcal, B. M., Tatarchenko, M., Karaoğlu, S. & Gevers, T. (2024). SceneTeller: Language-to-3D Scene Generation. ECCV. PDF |
2024 Pan, C., Yaman, B., Nesti, T., Mallik, A., Allievi, A., Velipasalar, S. & Ren, L. (2024). VLP: Vision Language Planning for Autonomous Driving. CVPR. PDF |
2024 Pan, C., Yaman, B., Velipasalar, S. & Ren, L. (2024). CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow. CVPR. PDF |
2024 Pan, J., Falkener, S., Berkenkamp, F. & Vanschoren, J. (2024). MALIBO: Meta-learning for Likelihood-free Bayesian Optimization. ICML. PDF |
2024 Potyka, N., Zhu, Y., He, Y., Kharlamov, E. & Staab, S. (2024). Robust Knowledge Extraction from Large Language Models using Social Choice Theory. AAMAS. PDF |
2024 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. |
2024 Schrader, T., Lange, L., Razniewski, S. & Friedrich, A. (2024). QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios. EMNLP. PDF |
2024 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 |
2024 Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. (2024). A Simple and Effective Pruning Approach for Large Language Models. ICLR. PDF |
2024 Tebbe, J., Zimmer, C., Steland, A., Lange-Hegermann, M. & Mies, F. (2024). Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning. AISTATS. |
2024 Tighineanu, P., Grossberger, L., Baireuther, P., Skubch, K., Falkner, S., Vinogradska, J. & Berkenkamp, F. Scalable (2024). Meta-Learning with Gaussian Processes. AISTATS. PDF |
2024 Wang, J., Laube, K. A., Li, Y., Hendrik Metzen, J., Cheng, S., Borges, J. & Khoreva, A. (2024). Label-free Neural Semantic Image Synthesis. ECCV. |
2024 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 |
2024 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 |
2024 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 |
2024 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. |
2024 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 |
2024 Zhuang, Z., Nicolae, M. & Fritz, M. (2024). Stealthy Imitation: Reward-guided Environment-free Policy Stealing. ICML. PDF |
2023 |
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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 |
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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. |
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2019 Blaiotta, C. (2019). Learning Generative Socially Aware Models of Pedestrian Motion. IEEE Robotics and Automation Letters, 4(4), 3433–3440. [Pdf] |
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2019 Delhaisse, B., Rozo, L., & Caldwell, D. G. (2019). PyRoboLearn: A Python Framework for Robot Learning Practitioners. CoRL. [Pdf] |
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2019 Doerr, A., Volpp, M., Toussaint, M., Trimpe, S., & Daniel, C. (2019). Trajectory-Based Off-Policy Deep Reinforcement Learning. ICML. [Pdf] |
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2018 |
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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] |
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2018 Etesami, J., Habibnia, A., & Kiyavash, N. (2018). Econometric Modeling of Systemic Risk: A Time Series Approach. KDD. [Pdf] |
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2017 |
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2017 Doerr, A., Daniel, C., Nguyen-Tuong, D., Marco, A., Schaal, S., Toussaint, M., & Trimpe, S. (2017). Optimizing Long-term Predictions for Model-based Policy Search. CoRL. [Pdf] |
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2017 Gondal, W. M., Köhler, J. M., Grzeszick, R., Fink, G. A., & Hirsch, M. (2017). Weakly-Supervised Localization of Diabetic Retinopathy Lesions in Retinal Fundus Images. ICIP. [Pdf] |
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2017 Schillinger, M., Hartmann, B., Skalecki, P., Meister, M., Nguyen-Tuong, D., & Nelles, O. (2017). Safe Active Learning and Bayesian Optimization for Tuning a PI-Controller. IFAC. [Pdf] |
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2016 |
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2016 Daniel, C., van Hoof, H., Peters, J., & Neumann, G. (2016). Probabilistic inference for determining options in reinforcement learning. Machine Learning, 104(2–3), 337–357. [Pdf] |
2016 Dhar, S., Naveen, N., Cherkassky, V., & Shah, M. (2016). Universum Learning for Multiclass SVM. SVM. [Pdf] |
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2016 Heit, J., Liu, J., & Shah, M. (2016a). An Architecture for the Deployment of Statistical Models for the Big Data Era. IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7840745&tag=1 |
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2016 Wagner, J., Fischer, V., Herman, M., & Behnke, S. (2016). Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks. ESANN. [Pdf] |
2015 |
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2015 Dhar, S., Ramakrishnan, C. Y. N., & Shah, M. (2015). ADMM based Scalable Machine Learning on Spark. ADMM. [Pdf] |
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] |
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2014 |
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2014 Bischoff, B., Nguyen-Tuong, D., Koller, T., Markert, H., & Knoll, A. (2014). Learning Throttle Valve Control Using Policy Search. ECML. [Pdf] |
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2014 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 |
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2013 Bischoff, B., Markert, H., Knoll, A., & Nguyen-Tuong, D. (2013). Solving the 15-Puzzle Game Using Local Value-Iteration. SCAI. [Pdf] |
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 |
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2012 Bischoff, B., Nguyen-Tuong, D., Streichert, F., Ewert, M., & Knoll, A. (2012). Fusing Vision and Odometry for Accurate Indoor Robot Localization. ICARCV. [Pdf] |
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