ei
Babbar, R., Muandet, K., Schölkopf, B.
TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification
Proceedings of the 2016 SIAM International Conference on Data Mining (SDM), pages: 234-242, (Editors: Sanjay Chawla Venkatasubramanian and Wagner Meira), May 2016 (conference)
am
ei
Büchler, D., Ott, H., Peters, J.
A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 4086-4092, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (conference)
ei
pn
Bartels, S., Hennig, P.
Probabilistic Approximate Least-Squares
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 51, pages: 676-684, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C. ), May 2016 (conference)
ei
Jayaram, V., Grosse-Wentrup, M.
A Transfer Learning Approach for Adaptive Classification in P300 Paradigms
Sixth International BCI Meeting (BCI), May 2016 (conference)
ei
Modugno, V., Neumann, G., Rueckert, E., Oriolo, G., Peters, J., Ivaldi, S.
Learning soft task priorities for control of redundant robots
IEEE International Conference on Robotics and Automation (ICRA), pages: 221-226, IEEE, May 2016 (conference)
ei
Tabibian, B., Farajtabar, M., Valera, I., Song, L., Schölkopf, B., Gomez Rodriguez, M.
On the Reliability of Information and Trustworthiness of Web Sources in Wikipedia
Wikipedia workshop at the 10th International AAAI Conference on Web and Social Media (ICWSM), May 2016 (conference)
ei
Sajjadi, M. S. M., Alamgir, M., von Luxburg, U.
Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines
Proceedings of the 3rd ACM conference on Learning @ Scale, pages: 369-378, (Editors: Haywood, J. and Aleven, V. and Kay, J. and Roll, I.), ACM, L@S, April 2016, (An earlier version of this paper had been presented at the ICML 2015 workshop for Machine Learning for Education.) (conference)
ei
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, 7(2):article no. 13, January 2016 (article)
ei
Borgström, J., Gordon, A. D., Ouyang, L., Russo, C., Ścibior, A., Szymczak, M.
Fabular: Regression Formulas As Probabilistic Programming
Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL), pages: 271-283, POPL ’16, ACM, January 2016 (conference)
ei
Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J.
Special Issue on Causal Discovery and Inference
ACM Transactions on Intelligent Systems and Technology (TIST), 7(2), January 2016, (Guest Editors) (misc)
ei
pn
Klenske, E. D., Zeilinger, M., Schölkopf, B., Hennig, P.
Gaussian Process-Based Predictive Control for Periodic Error Correction
IEEE Transactions on Control Systems Technology , 24(1):110-121, 2016 (article)
ei
Empirical Inference (2010-2015)
Scientific Advisory Board Report, 2016 (misc)
ei
Townsend, J., Koep, N., Weichwald, S.
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
Journal of Machine Learning Research, 17(137):1-5, 2016 (article)
ei
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, 128(967):094503, 2016 (article)
am
ei
Daniel, C., van Hoof, H., Peters, J., Neumann, G.
Probabilistic Inference for Determining Options in Reinforcement Learning
Machine Learning, Special Issue, 104(2):337-357, (Editors: Gärtner, T., Nanni, M., Passerini, A. and Robardet, C.), European Conference on Machine Learning im Machine Learning, Journal Track, 2016, Best Student Paper Award of ECML-PKDD 2016 (article)
ei
Katiyar, P., Castaneda, S., Patzwaldt, K., Russo, F., Poli, S., Ziemann, U., Disselhorst, J. A., Pichler, B. J.
Novel Random Forest based framework enables the segmentation of cerebral ischemic regions using multiparametric MRI
European Molecular Imaging Meeting, 2016 (poster)
ei
Logothetis, N. K., Murayama, Y., Ramirez-Villegas, J. F., Besserve, M., Evrard, H.
PGO wave-triggered functional MRI: mapping the networks underlying synaptic consolidation
47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)
ei
Mittal, A., Raj, A., Namboodiri, V. P., Tuytelaars, T.
Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set
2016 (misc)
ei
Rothkegel, L. O. M., Trukenbrod, H. A., Schütt, H. H., Wichmann, F. A., Engbert, R.
Influence of initial fixation position in scene viewing
Vision Research, 129, pages: 33-49, 2016 (article)
ei
Wallis, T. S. A., Bethge, M., Wichmann, F. A.
Testing models of peripheral encoding using metamerism in an oddity paradigm
Journal of Vision, 16(2), 2016 (article)
ei
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 Science, 113(27):7391-7398, 2016 (article)
ei
pn
Klenske, E. D., Hennig, P.
Dual Control for Approximate Bayesian Reinforcement Learning
Journal of Machine Learning Research, 17(127):1-30, 2016 (article)
ei
Divine, M. R., Katiyar, P., Kohlhofer, U., Quintanilla-Martinez, L., Disselhorst, J. A., Pichler, B. J.
A Population Based Gaussian Mixture Model Incorporating 18F-FDG-PET and DW-MRI Quantifies Tumor Tissue Classes
Journal of Nuclear Medicine, 57(3):473-479, 2016 (article)
ei
Zhang, K., Hyvärinen, A.
Nonlinear functional causal models for distinguishing cause from effect
In Statistics and Causality: Methods for Applied Empirical Research, pages: 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (inbook)
ei
Seith, F., Gatidis, S., Schmidt, H., Bezrukov, I., la Fougère, C., Nikolaou, K., Pfannenberg, C., Schwenzer, N.
Comparison of Positron Emission Tomography Quantification Using Magnetic Resonance–and Computed Tomography–Based Attenuation Correction in Physiological Tissues and Lesions: A Whole-Body Positron Emission Tomography/Magnetic Resonance Study in 66 Patients
Investigative Radiology, 51(1):66-71, 2016 (article)
ei
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, 17(90):1-29, 2016 (article)
ei
Castaneda, S., Katiyar, P., Russo, F., Calaminus, C., Disselhorst, J. A., Ziemann, U., Kohlhofer, U., Quintanilla-Martinez, L., Poli, S., Pichler, B. J.
Analysis of multiparametric MRI using a semi-supervised random forest framework allows the detection of therapy response in ischemic stroke
World Molecular Imaging Conference, 2016 (talk)
ei
Schütt, H. H., Harmeling, S., Macke, J. H., Wichmann, F. A.
Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data
Vision Research, 122, pages: 105-123, 2016 (article)
ei
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, pages: 825-833, 2016 (article)
ei
Köhler, R.
Advances in computational imaging: Benchmarking Deblurring Algorithms, Deep Neural Inpainting, Depth Estimation from Light Fields
Eberhard Karls Universität Tübingen, Germany, 2016 (phdthesis)
ei
Hohmann, M., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.
A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis
In Brain-Computer Interfaces: Lab Experiments to Real-World Applications, 228(Supplement C):221-239, 8, Progress in Brain Research, (Editors: Damien Coyle), Elsevier, 2016 (incollection)
ei
Daniel, C., Neumann, G., Kroemer, O., Peters, J.
Hierarchical Relative Entropy Policy Search
Journal of Machine Learning Research, 17(93):1-50, 2016 (article)
ei
Kiefel, M.
Tractable Structured Prediction using the Permutohedral Lattice
ETH Zurich, 2016 (phdthesis)
ei
Muandet, K., Sriperumbudur, B., Fukumizu, K., Gretton, A., Schölkopf, B.
Kernel Mean Shrinkage Estimators
Journal of Machine Learning Research, 17(48):1-41, 2016 (article)
ei
Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B.
Learning to Deblur
IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (article)
ei
Jayaram, V., Alamgir, M., Altun, Y., Schölkopf, B., Grosse-Wentrup, M.
Transfer Learning in Brain-Computer Interfaces
IEEE Computational Intelligence Magazine, 11(1):20-31, 2016 (article)
ei
Weichwald, S., Grosse-Wentrup, M., Gretton, A.
MERLiN: Mixture Effect Recovery in Linear Networks
IEEE Journal of Selected Topics in Signal Processing, 10(7):1254-1266, 2016 (article)
ei
Peters, J., Bühlmann, P., Meinshausen, N.
Causal inference using invariant prediction: identification and confidence intervals
Journal of the Royal Statistical Society, Series B (Statistical Methodology), 78(5):947-1012, 2016, (with discussion) (article)
ei
Spirtes, P., Zhang, K.
Causal discovery and inference: concepts and recent methodological advances
Applied Informatics, 3(3):1-28, 2016 (article)
ei
Fomina, T., Lohmann, 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, 13(6):066021, 2016 (article)
ei
Castaneda, S., Katiyar, P., Russo, F., Maurer, A., Patzwaldt, K., Poli, S., Calaminus, C., Disselhorst, J. A., Ziemann, U., Pichler, B. J.
Multiparametric Imaging of Ischemic Stroke using [89Zr]-Desferal-EPO-PET/MRI in combination with Gaussian Mixture Modeling enables unsupervised lesions identification
European Molecular Imaging Meeting, 2016 (poster)
ei
Gomez-Rodriguez, M., Song, L., Du, N., Zha, H., Schölkopf, B.
Influence Estimation and Maximization in Continuous-Time Diffusion Networks
ACM Transactions on Information Systems, 34(2):9:1-9:33, 2016 (article)
am
ei
Büchler, D., Calandra, R., Peters, J.
Modeling Variability of Musculoskeletal Systems with Heteroscedastic Gaussian Processes
Workshop on Neurorobotics, Neural Information Processing Systems (NIPS), 2016 (conference)
ei
Stimper, V.
Zwischen Harmonie und Chaos - ein verallgemeinertes Modell des Doppelpendels
Junge Wissenschaft, (109), 2016 (article)
ei
Foreman-Mackey, D., Morton, T. D., Hogg, D. W., Agol, E., Schölkopf, B.
The population of long-period transiting exoplanets
The Astronomical Journal, 152(6):206, 2016 (article)
ei
Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.
Statistical source separation of rhythmic LFP patterns during sharp wave ripples in the macaque hippocampus
47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)
ei
Raj, A., Olbrich, J., Gärtner, B., Schölkopf, B., Jaggi, M.
Screening Rules for Convex Problems
2016 (unpublished) Submitted
ei
Jäkel, F., Singh, M., Wichmann, F. A., Herzog, M. H.
An overview of quantitative approaches in Gestalt perception
Vision Research, 126, pages: 3-8, 2016 (article)