Max Planck Institute for Intelligent Systems

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Journal Article (60)

  1. 2017
    Grimm, D.; Roqueiro, D.; Salome, P.; Kleeberger, S.; Greshake, B.; Zhu, W.; Liu, C.; Lippert, C.; Stegle, O.; Schölkopf, B. et al.; Weigel, D.; Borgwardt, K.: easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies. The Plant Cell 29 (1), pp. 5 - 19 (2017)
  2. 2016
    Fomina, T.; Lohmann G, G.; Erb, M.; Ethofer, T.; Schölkopf, B.; Grosse-Wentrup, M.: Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS. Journal of Neural Engineering (2016)
  3. Foreman-Mackey, D.; Morton, T. D.; Hogg, D. W.; Agol, E.; Schölkopf, B.: The population of long-period transiting exoplanets. The Astrophysical Journal 152 (6), 206 (2016)
  4. Gomez-Rodriguez, M.; Song, L.; Daneshmand, H.; Schölkopf, B.: Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm. Journal of Machine Learning Research (2016)
  5. Grosse-Wentrup, M.; Janzing, D.; Siegel, M.; Schölkopf, B.: Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach. NeuroImage 125, pp. 825 - 833 (2016)
  6. Janzing, D.; Chaves, R.; Schölkopf, B.: Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference. New Journal of Physics 18 (9), 093052 (2016)
  7. Jayaram, V.; Alamgir, M.; Altun, Y.; Schölkopf, B.; Grosse-Wentrup, M.: Transfer Learning in Brain-Computer Interfaces. IEEE Computational Intelligence Magazine (2016)
  8. Klenske, E.; Zeilinger, M.; Schölkopf, B.; Hennig, P.: Gaussian Process Based Predictive Control for Periodic Error Correction. IEEE Transactions on Control Systems Technology 24 (1), pp. 110 - 121 (2016)
  9. Mooij, J.M.; Peters, J.; Janzing, D.; Zscheischler, J.; Schölkopf, B.: Distinguishing cause from effect using observational data: methods and benchmarks. The Journal of Machine Learning Research 17 (1), pp. 1103 - 1204 (2016)
  10. Muandet, K.; Sriperumbudur, B. K.; Fukumizu, K.; Gretton, A.; Schölkopf, B.: Kernel Mean Shrinkage Estimators. The Journal of Machine Learning Research (2016)
  11. Schuler, C. J.; Hirsch, M.; Harmeling, S.; Schölkopf, B.: Learning to Deblur. IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)
  12. Schölkopf, B.; Hogg, D.; Wang, D.; Foreman-Mackey, D.; Janzing, D.; Simon-Gabriel, C.-J.; Peters, J.: Modeling Confounding by Half-Sibling Regression. Proceedings of the National Academy of Sciences of the United States of America (2016)
  13. Wang, D.; Hogg, D. W.; Foreman-Mackey, D.; Schölkopf, B.: A Causal, Data-driven Approach to Modeling the Kepler Data. Publications of the Astronomical Society of the Pacific (2016)
  14. Zhang, K.; Li, J.; Bareinboim, E.; Schölkopf, B.; Pearl, J.: Preface to the ACM TIST Special Issue on Causal Discovery and Inference. ACM Transactions on Intelligent Systems and Technology 7 (2), 17 (2016)
  15. Zhang, K.; Wang, Z.; Zhang, J.; Schölkopf, B.: On estimation of functional causal models: General results and application to post-nonlinear causal model. ACM Transactions on Intelligent Systems and Technologies (2016)
  16. 2015
    Besserve, M.; Lowe, S. C.; Logothetis, N. K.; Schölkopf, B.; Panzeri, S.: Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer. PLoS Biology (2015)
  17. Foreman-Mackey, D.; Montet, B.T.; Hogg, D.W.; Morton, T.D.; Wang; Schölkopf, B.: A systematic search for transiting planets in the K2 data. The Astrophysical Journal (2015)
  18. Janzing, D.; Schölkopf, B.: Semi-Supervised Interpolation in an Anticausal Learning Scenario. Journal of Machine Learning Research (JMLR) (2015)
  19. Kopp, M.; Harmeling, S.; Schütz, G.; Schölkopf, B.; Fähnle, M.: Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter. Ultramicroscopy 148, pp. 115 - 122 (2015)
  20. Küffner, R.; Zach , N.; Norel, R.; Hawe, J.; Schoenfeld , D.; Wang , L.; Li , G.; Fang, L.; Mackey, L.; Hardiman, O. et al.; Cudkowicz, M.; Sherman, A.; Ertaylan, G.; Grosse-Wentrup, M.; Hothorn, T.; van Ligtenberg, J.; Macke, J.; Meyer, T.; Schölkopf, B.; Tran, L.; Vaughan, R.; Stolovitzky, G.; Leitner, M.: Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nature Biotechnology 33, pp. 51 - 57 (2015)
 
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