943 results (BibTeX)

2017


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]

2017

[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]


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


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]

2016

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]


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]


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]


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]


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]


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]


Domain Adaptation with Conditional Transferable Components

Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., Schölkopf, B.

Proceedings of the 33nd International Conference on Machine Learning (ICML 2016), 48, pages: 2839-2848, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M.-F. and Weinberger, K. Q.), 2016 (conference)

link (url) [BibTex]

link (url) [BibTex]


An Improved Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Hohmann, M., Fomina, T., Jayaram, V., Förster, C., Just, J., M., S., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

Proceedings of the Sixth International BCI Meeting, pages: 44, (Editors: Müller-Putz, G. R. and Huggins, J. E. and Steyrl, D.), BCI, 2016 (conference)

DOI [BibTex]

DOI [BibTex]


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Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines

Sajjadi, M., Alamgir, M., von Luxburg, U.

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, 2016, (An earlier version of this paper had been presented at the ICML 2015 workshop for Machine Learning for Education.) (conference)

Arxiv [BibTex]

Arxiv [BibTex]


A Transfer Learning Approach for Adaptive Classification in P300 Paradigms

Jayaram, V., Grosse-Wentrup, M.

Proceedings of the Sixth International BCI Meeting, BCI, 2016 (conference) Accepted

[BibTex]

[BibTex]


Unifying distillation and privileged information

Lopez-Paz, D., Schölkopf, B., Bottou, L., Vapnik, V.

International Conference on Learning Representations, ICLR, 2016 (conference) Accepted

[BibTex]

[BibTex]


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A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning

Büchler, D., Ott, H., Peters, J.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2016, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings) Accepted

ICRA16final [BibTex]

ICRA16final [BibTex]


A Scalable Mixed-norm Approach for Learning Lightweight Models in Large-scale Classification

Babbar, R., Muandet, K., Schölkopf, B.

Proceedings of the 2016 SIAM International Conference on Data Mining, pages: 234-242, (Editors: Sanjay Chawla Venkatasubramanian and Wagner Meira Jr.), SDM, 2016 (conference)

DOI [BibTex]

DOI [BibTex]

2015


BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions

Rothenhäusler, D., Heinze, C., Peters, J., Meinshausen, N.

Advances in Neural Information Processing Systems 28, pages: 1513-1521, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (conference)

link (url) [BibTex]

2015

link (url) [BibTex]


Retrospective motion correction of magnitude-input MR images

Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B.

International Conference on Machine Learning (ICML) 2015, Workshop on Machine Learning meets Medical Imaging, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, First International Workshop, MLMMI, July 2015 (conference)

DOI [BibTex]

DOI [BibTex]


Is Breathing Rate a Confounding Variable in Brain-Computer Interfaces (BCIs) Based on EEG Spectral Power?

Ibarra Chaoul, A., Grosse-Wentrup, M.

Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages: 1079-1082, EMBC, August 2015 (conference)

DOI [BibTex]

DOI [BibTex]


Self-calibration of optical lenses

Hirsch, M., Schölkopf, B.

In IEEE International Conference on Computer Vision (ICCV 2015), pages: 612-620, IEEE, 2015 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

In Advances in Neural Information Processing Systems 28, pages: 181-189, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (inproceedings)

Abstract
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent. [You can find the matlab research code under `attachments' below. The zip-file contains a minimal working example. The docstring in probLineSearch.m contains additional information. A more polished implementation in C++ will be published here at a later point. For comments and questions about the code please write to mmahsereci@tue.mpg.de.]

Matlab research code link (url) Project Page [BibTex]

Matlab research code link (url) Project Page [BibTex]


Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills

Wahrburg, A., Zeiss, S., Matthias, B., Peters, J., Ding, H.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 852-857, IROS, September 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Stabilizing Novel Objects by Learning to Predict Tactile Slip

Veiga, F., van Hoof, H., Peters, J., Hermans, T.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 5065-5072, IROS, September 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Extracting Low-Dimensional Control Variables for Movement Primitives

Rueckert, E., Mundo, J., Paraschos, A., Peters, J., Neumann, G.

In IEEE International Conference on Robotics and Automation, pages: 1511-1518, ICRA, 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Reinforcement Learning vs Human Programming in Tetherball Robot Games

Parisi, S., Abdulsamad, H., Paraschos, A., Daniel, C., Peters, J.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 6428-6434, IROS, September 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Model-Free Probabilistic Movement Primitives for Physical Interaction

Paraschos, A., Rueckert, E., Peters, J., Neumann, G.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 2860-2866, IROS, September 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives

Manschitz, S., Kober, J., Gienger, M., Peters, J.

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 449-455, IROS, September 2015 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation

Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Peters, J.

In Proceedings of the International Symposium of Robotics Research, ISRR, 2015 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]