2312 results (BibTeX)

Dynamic Time-of-Flight

Schober, M., Adam, A., Yair, O., Mazor, S., Nowozin, S.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (conference) Accepted

[BibTex]

[BibTex]


Discovering Causal Signals in Images

Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., Bottou, L.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (conference) Accepted

[BibTex]

[BibTex]


Flexible Spatio-Temporal Networks for Video Prediction

Lu, C., Hirsch, M., Schölkopf, B.

The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (conference) Accepted

[BibTex]

[BibTex]


Frequency Peak Features for Low-Channel Classification in Motor Imagery Paradigms

Jayaram, V., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 8th International IEEE EMBS Conference on Neural Engineering (NER 2017), 2017 (conference) Accepted

[BibTex]

[BibTex]


AdaGAN: Boosting Generative Models

Tolstikhin, I., Gelly, S., Bousquet, O., Simon-Gabriel, C., Schölkopf, B.

2017 (techreport) Submitted

Arxiv [BibTex]

Arxiv [BibTex]


DeepCoder: Learning to Write Programs

Balog, M., Gaunt, A., Brockschmidt, M., Nowozin, S., Tarlow, D.

5th International Conference on Learning Representations (ICLR), 2017 (conference) Accepted

Arxiv [BibTex]

Arxiv [BibTex]


Multi-frame blind image deconvolution through split frequency - phase recovery

Gauci, A., Abela, J., Cachia, E., Hirsch, M., ZarbAdami, K.

Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), pages: 1022511, (Editors: Yulin Wang, Tuan D. Pham, Vit Vozenilek, David Zhang, Yi Xie), 2017 (conference)

DOI [BibTex]

DOI [BibTex]


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Distilling Information Reliability and Source Trustworthiness from Digital Traces

Tabibian, B., Valera, I., Farajtabar, M., Song, L., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 26th International Conference on World Wide Web (WWW2017), 2017 (conference) Accepted

Project [BibTex]

Project [BibTex]


DiSMEC – Distributed Sparse Machines for Extreme Multi-label Classification

Babbar, R., Schölkopf, B.

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM 2017), pages: 721-729, 2017 (conference)

DOI [BibTex]

DOI [BibTex]

2016


Multiparametric Imaging of Ischemic Stroke using [89Zr]-Desferal-EPO-PET/MRI in combination with Gaussian Mixture Modeling enables unsupervised lesions identification

Castaneda, S., Katiyar, P., Russo, F., Maurer, A., Patzwaldt, K., Poli, S., Calaminus, C., Disselhorst, J., Ziemann, U., Pichler, B.

European Molecular Imaging Meeting, 2016 (poster)

link (url) [BibTex]

2016

link (url) [BibTex]


Analysis of multiparametric MRI using a semi-supervised random forest framework allows the detection of therapy response in ischemic stroke

Castaneda, S., Katiyar, P., Russo, F., Calaminus, C., Disselhorst, J., Ziemann, U., Kohlhofer, U., Quintanilla-Martinez, L., Poli, S., Pichler, B.

World Molecular Imaging Conference, 2016 (talk)

link (url) [BibTex]

link (url) [BibTex]


Novel Random Forest based framework enables the segmentation of cerebral ischemic regions using multiparametric MRI

Katiyar, P., Castaneda, S., Patzwaldt, K., Russo, F., Poli, S., Ziemann, U., Disselhorst, J., Pichler, B.

European Molecular Imaging Meeting, 2016 (poster)

link (url) [BibTex]

link (url) [BibTex]


Multi-view learning on multiparametric PET/MRI quantifies intratumoral heterogeneity and determines therapy efficacy

Katiyar, P., Divine, M., Kohlhofer, U., Quintanilla-Martinez, L., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B., Disselhorst, J.

World Molecular Imaging Conference, 2016 (talk)

link (url) [BibTex]

link (url) [BibTex]


Spectral Clustering predicts tumor tissue heterogeneity using dynamic 18F-FDG PET: a complement to the standard compartmental modeling approach

Katiyar, P., Divine, M., Kohlhofer, U., Quintanilla-Martinez, L., Schölkopf, B., Pichler, B., Disselhorst, J.

Journal of Nuclear Medicine, 2016, (published ahead of print November 3, 2016) (article)

DOI [BibTex]

DOI [BibTex]


A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

Katiyar, P., Divine, M., Kohlhofer, U., Quintanilla-Martinez, L., Schölkopf, B., Disselhorst, J.

Molecular Imaging and Biology, pages: 1-7, 2016 (article)

DOI [BibTex]

DOI [BibTex]


Experimental and causal view on information integration in autonomous agents

Geiger, P., Hofmann, K., Schölkopf, B.

Proceedings of the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), pages: 21-28, (Editors: Hatzilygeroudis, I. and Palade, V.), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


The Mondrian Kernel

Balog, M., Lakshminarayanan, B., Ghahramani, Z., Roy, D., Teh, Y.

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI), (Editors: Ihler, Alexander T. and Janzing, Dominik), 2016 (conference)

Arxiv link (url) [BibTex]

Arxiv link (url) [BibTex]


Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs

Xiao, L., Wang, J., Heidrich, W., Hirsch, M.

Computer Vision - ECCV 2016, Lecture Notes in Computer Science, LNCS 9907, Part III, pages: 734-749, (Editors: Bastian Leibe, Jiri Matas, Nicu Sebe and Max Welling), Springer, 2016 (conference)

DOI [BibTex]

DOI [BibTex]


easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies

Grimm, D., Roqueiro, D., Salome, P., Kleeberger, S., Greshake, B., Zhu, W., Liu, C., Lippert, C., Stegle, O., Schölkopf, B., Weigel, D., Borgwardt, K.

The Plant Cell, 2016 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS

Fomina, T., Lohmann, G., Erb, M., Ethofer, T., Schölkopf, B., Grosse-Wentrup, M.

Journal of Neural Engineering, 13(6):066021, 2016 (article)

link (url) [BibTex]

link (url) [BibTex]


BundleMAP: Anatomically Localized Classification, Regression, and Hypothesis Testing in Diffusion MRI

Khatami, M., Schmidt-Wilcke, T., Sundgren, P., Abbasloo, A., Schölkopf, B., Schultz, T.

Pattern Recognition, 2016 (article) In press

DOI [BibTex]

DOI [BibTex]


Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Tolstikhin, I., Sriperumbudur, B., Schölkopf, B.

Advances in Neural Information Processing Systems 29, 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference) Accepted

[BibTex]

[BibTex]


Consistent Kernel Mean Estimation for Functions of Random Variables

Scibior, A., Simon-Gabriel, C., Tolstikhin, I., Schölkopf, B.

Advances in Neural Information Processing Systems 29, pages: 1732-1740, (Editors: D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett), 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


End-to-End Learning for Image Burst Deblurring

Wieschollek, P., Schölkopf, B., Lensch, H., Hirsch, M.

Computer Vision - ACCV 2016 - 13th Asian Conference on Computer Vision, 2016 (conference) Accepted

[BibTex]

[BibTex]


The population of long-period transiting exoplanets

Foreman-Mackey, D., Morton, T., Hogg, D., Agol, E., Schölkopf, B.

The Astrophysical Journal, 2016 (article) Accepted

[BibTex]

[BibTex]


Multi-task logistic regression in brain-computer interfaces

Fiebig, K., Jayaram, V., Peters, J., Grosse-Wentrup, M.

Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), IEEE, 2016 (conference) To be published

link (url) [BibTex]

link (url) [BibTex]


Jointly Learning Trajectory Generation and Hitting Point Prediction in Robot Table Tennis

Huang, Y., Büchler, D., Koc, O., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots, Humanoids, 2016 (conference) Accepted

[BibTex]

[BibTex]


Using Probabilistic Movement Primitives for Striking Movements

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

16th IEEE-RAS International Conference on Humanoid Robots, Humanoids, 2016 (conference) Accepted

[BibTex]

[BibTex]


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Depth Estimation Through a Generative Model of Light Field Synthesis

Sajjadi, M., Köhler, R., Schölkopf, B., Hirsch, M.

Pattern Recognition: 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings, 9796, pages: 426-438, Lecture Notes in Computer Science, (Editors: Rosenhahn, B. and Andres, B.), Springer International Publishing, 2016 (conference)

Arxiv link (url) DOI [BibTex]

Arxiv link (url) DOI [BibTex]


A New Trajectory Generation Framework in Robotic Table Tennis

Koc, O., Maeda, G., Peters, J.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IROS, 2016 (conference) Accepted

[BibTex]

[BibTex]


Probabilistic Inference for Determining Options in Reinforcement Learning

Daniel, C., van Hoof, H., Peters, J., Neumann, G.

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 ECMLPKDD 2016 (article)

DOI [BibTex]

DOI [BibTex]


Active Nearest-Neighbor Learning in Metric Spaces

Kontorovich, A., Sabato, S., Urner, R.

Advances in Neural Information Processing Systems 29, 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference) Accepted

[BibTex]

[BibTex]


Lifelong Learning with Weighted Majority Votes

Pentina, A., Urner, R.

Advances in Neural Information Processing Systems 29, 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016 (conference) Accepted

[BibTex]

[BibTex]


Unsupervised clustering of EOG as a viable substitute for optical eye-tracking

Flad, N., Fomina, T., Bülthoff, H., Chuang, L.

First Workshop on Eye Tracking and Visualization (ETVIS 2015), (Editors: Weiskopf, D., Burch, M., Chuang, L., Fischer, B., and Schmidt, A.), Springer, 2016 (conference) In press

[BibTex]

[BibTex]


A Causal, Data-driven Approach to Modeling the Kepler Data

Wang, D., Hogg, D., Foreman-Mackey, D., Schölkopf, B.

Publications of the Astronomical Society of the Pacific, 128(967):094503, 2016 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


The Arrow of Time in Multivariate Time Serie

Bauer, S., Schölkopf, B., Peters, J.

Proceedings of the 33rd International Conference on Machine Learning, 48, pages: 2043-2051, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M. F. and Weinberger, K. Q.), JMLR, ICML, 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


Batch Bayesian Optimization via Local Penalization

González, J., Dai, Z., Hennig, P., Lawrence, N.

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016), 51, pages: 648-657, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C.), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


Active Uncertainty Calibration in Bayesian ODE Solvers

Kersting, H., Hennig, P.

Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), pages: 309-318, (Editors: Ihler, A. and Janzing, D.), AUAI Press, 2016 (conference)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


On Version Space Compression

Ben-David, S., Urner, R.

Algorithmic Learning Theory - 27th International Conference (ALT 2016), 2016 (conference) Accepted

[BibTex]

[BibTex]


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Probabilistic Approximate Least-Squares

Bartels, S., Hennig, P.

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016), 51, pages: 676-684, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C. ), 2016 (conference)

Abstract
Least-squares and kernel-ridge / Gaussian process regression are among the foundational algorithms of statistics and machine learning. Famously, the worst-case cost of exact nonparametric regression grows cubically with the data-set size; but a growing number of approximations have been developed that estimate good solutions at lower cost. These algorithms typically return point estimators, without measures of uncertainty. Leveraging recent results casting elementary linear algebra operations as probabilistic inference, we propose a new approximate method for nonparametric least-squares that affords a probabilistic uncertainty estimate over the error between the approximate and exact least-squares solution (this is not the same as the posterior variance of the associated Gaussian process regressor). This allows estimating the error of the least-squares solution on a subset of the data relative to the full-data solution. The uncertainty can be used to control the computational effort invested in the approximation. Our algorithm has linear cost in the data-set size, and a simple formal form, so that it can be implemented with a few lines of code in programming languages with linear algebra functionality.

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection

Zhang, K., Zhang, J., Huang, B., Schölkopf, B., Glymour, C.

Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), pages: 825-834, (Editors: Ihler, A. and Janzing, D.), AUAI Press, 2016, plenary presentation (conference)

link (url) [BibTex]

link (url) [BibTex]


Learning Causal Interaction Network of Multivariate Hawkes Processes

Etesami, S., Kiyavash, N., Zhang, K., Singhal, K.

Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016, poster presentation (conference)

[BibTex]

[BibTex]


Causal discovery and inference: concepts and recent methodological advances

Spirtes, P., Zhang, K.

Applied Informatics, 3(3):1-28, 2016 (article)

DOI [BibTex]

DOI [BibTex]


Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data

Weichwald, S., Gretton, A., Schölkopf, B., Grosse-Wentrup, M.

Proceedings of the 6th International Workshop on Pattern Recognition in NeuroImaging (PRNI 2016), 2016 (conference)

PDF Arxiv Code DOI [BibTex]

PDF Arxiv Code DOI [BibTex]