Researcher Portfolio
Schölkopf, Bernhard
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, Max Planck Institute for Biological Cybernetics, Max Planck Society
Researcher Profile
Position: Max Planck Institute for Biological Cybernetics, Max Planck Society
Position: Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society
Position: Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society
Position: Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society
Additional IDs: ORCID:
https://orcid.org/0000-0002-8177-0925
MPIKYB: bs
MPIKYB: bs
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons84193
External references
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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]