Researcher Portfolio
Strotmann, Barbara
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society
Researcher Profile
Position: Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons20029
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Publications
(1 - 25 of 956)
: Van Bastelaer, M.-O., Kremer, H., Volchkov, V. V., Passy, J.-C., & Schölkopf, B. (2023). Glare Removal for Astronomical Images with High Local Dynamic Range. In 2023 IEEE International Conference on Computational Photography (ICCP). IEEE. [PubMan] : Spieler, A., Rahaman, N., Martius, G., Schölkopf, B., & Levina, A. (2023). The Expressive Leaky Memory (ELM) neuron: a biologically inspired, computationally expressive, and efficient model of a cortical neuron. Poster presented at Bernstein Conference 2023, Berlin, Germany. [PubMan] : Katiyar, P., Schwenck, J., Frauenfeld, L., Divine, M. R., Agrawal, V., Kohlhofer, U., Gatidis, S., Kontermann, R., Königsrainer, A., Quintanilla-Martinez, L., la Fougère, C., Schölkopf, B., Pichler, B. J., & Disselhorst, J. A. (2023). Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET–MRI data. nature biomedical engineering, 7(8), 1014-1027. doi:10.1038/s41551-023-01047-9. [PubMan] : Dittrich, A., Schneider, J., Guist, S., Gürtler, N., Ott, H., Steinbrenner, T., Schölkopf, B., & Büchler, D. (2023). AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3058-3064). IEEE. [PubMan] : Paleyes, A., Guo, S., Schölkopf, B., & Lawrence, N. D. (2023). Dataflow graphs as complete causal graphs. In 2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN) (pp. 7-12). New York, NY: IEEE. [PubMan] : Ehyaei, A.-R., Karimi, A.-H., Schölkopf, B., & Maghsudi, S. (2023). Robustness Implies Fairness in Causal Algorithmic Recourse. In FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 984-1001). ACM. [PubMan] : Kekić, A., Dehning, J., Gresele, L., von Kügelgen, J., Priesemann, V., & Schölkopf, B. (2023). Evaluating vaccine allocation strategies using simulation-assisted causal modeling. Patterns, 4(6): 100739. doi:10.1016/j.patter.2023.100739. [PubMan] : Katiyar, P., Schwenck, J., Frauenfeld, L., Divine, M. R., Agrawal, V., Kohlhofer, U., Gatidis, S., Kontermann, R., Königsrainer, A., Quintanilla-Martinez, L., la Fougère, C., Schölkopf, B., Pichler, B. J., & Disselhorst, J. A. (2023). Quantification of intratumor heterogeneity using PET/MRI and multi-view learning: a translational study from mouse to human. Nature Biomedical Engineering, 7, 1014-1027. doi:10.1038/s41551-023-01047-9. [PubMan] : Karimi, A.-H., Barthe, G., Schölkopf, B., & Valera, I. (2023). A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations. ACM computing surveys, 55(5): 95. doi:10.1145/3527848. [PubMan] : Mineeva, O., Danciu, D., Schölkopf, B., Ley, R. E., Rätsch, G., & Youngblut, N. D. (2023). ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning. PLoS Computational Biology, 19(5): e1011001. doi:10.1371/journal.pcbi.1011001. [PubMan] : Mineeva, O., Danciu, D., Schölkopf, B., Ley, R., Rätsch, G., & Youngblut, N. (2023). ResMiCo: increasing the quality of metagenome-assembled genomes with deep learning. PLoS Computational Biology, 19(5): e1011001. doi:10.1371/journal.pcbi.1011001. [PubMan] : Wildberger, J., Dax, M., Green, S. R., Gair, J., Pürrer, M., Macke, J. H., Buonanno, A., & Schölkopf, B. (2023). Adapting to noise distribution shifts in flow-based gravitational-wave inference. Physical Review D, 107: 084046. doi:10.1103/PhysRevD.107.084046. [PubMan] : Dax, M., Green, S. R., Gair, J., Pürrer, M., Wildberger, J., Macke, J. H., Buonanno, A., & Schölkopf, B. (2023). Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. Physical Review Letters, 130: 171403. doi:10.1103/PhysRevLett.130.171403. [PubMan] : Simon-Gabriel, C.-J., Barp, A., Schölkopf, B., & Mackey, L. (2023). Metrizing Weak Convergence with Maximum Mean Discrepancies. Journal of Machine Learning Research, 24(184): 599. [PubMan] : Reizinger, P., Gresele, L., Brady, J., von Kügelgen, J., Zietlow, D., Schölkopf, B., Martius, G., Brendel, W., & Besserve, M. (2023). Embrace the Gap: VAEs Perform Independent Mechanism Analysis. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds. ), Advances in Neural Information Processing Systems 35 (pp. 12040-12057). Red Hook, NY: Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper_files/paper/2022/hash/4eb91efe090f72f7cf42c69aab03fe85-Abstract-Conference.html. [PubMan] : Athanassiadis, A. G., Schlieder, L., Melde, K., Volchkov, V. V., Schölkopf, B., & Fischer, P. (2023). Multiplane Diffractive Acoustic Networks. IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 70(5), 441-448. doi:10.1109/TUFFC.2023.3255992. [PubMan] : Melde, K., Kremer, H., Shi, M., Seneca, S., Frey, C., Platzman, I., Degel, C., Schmitt, D., Schölkopf, B., & Fischer, P. (2023). Compact holographic sound fields enable rapid one-step assembly of matter in 3D. Science Advances, 9(6): eadf618. doi:10.1126/sciadv.adf6182. [PubMan] : Brady, J., Zimmermann, R. S., Sharma, Y., Schölkopf, B., Kügelgen, J. v., & Brendel, W. (2023). Provably Learning Object-Centric Representations. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds. ), Proceedings of the 40th International Conference on Machine Learning (pp. 3038-3062). Cambridge, MA: JMLR. Retrieved from https://proceedings.mlr.press/v202/brady23a.html. [PubMan] : Agudelo-España, D., Nemmour, Y., Schölkopf, B., & Zhu, J.-J. (2023). Learning Random Feature Dynamics for Uncertainty Quantification. In 2022 IEEE 61st Conference on Decision and Control (CDC) (pp. 4937-4944). Piscataway, NJ: IEEE. doi:10.1109/CDC51059.2022.9993152. [PubMan] : Mehrjou, A., Iannelli, A., & Schölkopf, B. (2023). Learning Dynamical Systems using Local Stability Priors. Journal of Computational Dynamics, 10(1), 175-198. doi:10.3934/jcd.2022021. [PubMan] : Kladny, K.-R., von Kügelgen, J., Schölkopf, B., & Muehlebach, M. (2023). Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators. In R. J. Evans, & I. Shpitser (Eds. ), Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (pp. 1087-1097). PMLR. [PubMan] : Liu, Y., Alahi, A., Russell, C., Horn, M., Zietlow, D., Schölkopf, B., & Locatello, F. (2023). Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. In M. Van der Schaar, C. Zhang, & D. Janzing (Eds. ), Proceedings of the Second Conference on Causal Learning and Reasoning (pp. 553-573). PMLR. [PubMan] : Khadiv, M., Meduri, A., Zhu, H., Righetti, L., & Schölkopf, B. (2023). Learning Locomotion Skills from MPC in Sensor Space. In Proceedings of The 5th Annual Learning for Dynamics and Control Conference (pp. 1218-1230). PMLR. [PubMan] : Wildberger, J., Guo, S., Bhattacharyya, A., & Schölkopf, B. (2023). On the Interventional Kullback-Leibler Divergence. In M. Van der Schaar, C. Zhang, & D. Janzing (Eds. ), Proceedings of the Second Conference on Causal Learning and Reasoning (pp. 328-349). PMLR. [PubMan] : Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F., Schölkopf, B., & Russell, C. (2023). Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) (pp. 10400-10411). Piscataway, NJ: IEEE. doi:10.1109/CVPR52688.2022.01016. [PubMan]