Publications

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

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