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2014


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Quantifying Information Overload in Social Media and its Impact on Social Contagions

Gomez Rodriguez, M., Gummadi, K., Schölkopf, B.

In Proceedings of the Eighth International Conference on Weblogs and Social Media, pages: 170-179, (Editors: E. Adar, P. Resnick, M. De Choudhury, B. Hogan, and A. Oh), AAAI Press, ICWSM, 2014 (inproceedings)

Web [BibTex]

2014

Web [BibTex]


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Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm

Daneshmand, H., Gomez Rodriguez, M., Song, L., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 793-801, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Interaction Primitives for Human-Robot Cooperation Tasks

Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., Peters, J.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 2831-2837, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning to Predict Phases of Manipulation Tasks as Hidden States

Kroemer, O., van Hoof, H., Neumann, G., Peters, J.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 4009-4014, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Visualizing Uncertainty in HARDI Tractography Using Superquadric Streamtubes

Wiens, V., Schlaffke, L., Schmidt-Wilcke, T., Schultz, T.

In Eurographics Conference on Visualization, Short Papers, (Editors: Elmqvist, N. and Hlawitschka, M. and Kennedy, J.), EuroVis, 2014 (inproceedings)

Abstract
Standard streamtubes for the visualization of diffusion MRI data are rendered either with a circular or with an elliptic cross section whose aspect ratio indicates the relative magnitudes of the medium and minor eigenvalues. Inspired by superquadric tensor glyphs, we propose to render streamtubes with a superquadric cross section, which develops sharp edges to more clearly convey the orientation of the second and third eigenvectors where they are uniquely defined, while maintaining a circular shape when the smaller two eigenvalues are equal. As a second contribution, we apply our novel superquadric streamtubes to visualize uncertainty in the tracking direction of HARDI tractography, which we represent using a novel propagation uncertainty tensor.

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Permutation-Based Kernel Conditional Independence Test

Doran, G., Muandet, K., Zhang, K., Schölkopf, B.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014), pages: 132-141, (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon, UAI2014, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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A unifying view of representer theorems

Argyriou, A., Dinuzzo, F.

In Proceedings of the 31th International Conference on Machine Learning, 32, pages: 748-756, (Editors: Xing, E. P. and Jebera, T.), ICML, 2014 (inproceedings)

PDF PDF [BibTex]

PDF PDF [BibTex]


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Riemannian Sparse Coding for Positive Definite Matrices

Cherian, A., Sra, S.

In 13th European Conference on Computer Vision, LNCS 8691, pages: 299-314, (Editors: Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T.), Springer, ECCV, 2014 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Probabilistic ODE Solvers with Runge-Kutta Means

Schober, M., Duvenaud, D., Hennig, P.

In Advances in Neural Information Processing Systems 27, pages: 739-747, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Mask-Specific Inpainting with Deep Neural Networks

Köhler, R., Schuler, C., Schölkopf, B., Harmeling, S.

In Pattern Recognition (GCPR 2014), pages: 523-534, (Editors: X Jiang, J Hornegger, and R Koch), Springer, 2014, Lecture Notes in Computer Science (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Randomized Nonlinear Component Analysis

Lopez-Paz, D., Sra, S., Smola, A., Ghahramani, Z., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 1359-1367, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Causal and Anti-Causal Learning in Pattern Recognition for Neuroimaging

Weichwald, S., Schölkopf, B., Ball, T., Grosse-Wentrup, M.

In 4th International Workshop on Pattern Recognition in Neuroimaging (PRNI), IEEE , PRNI, 2014 (inproceedings)

PDF Arxiv DOI [BibTex]

PDF Arxiv DOI [BibTex]


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Bayesian Gait Optimization for Bipedal Locomotion

Calandra, R., Gopalan, N., Seyfarth, A., Peters, J., Deisenroth, M.

In Proceedings of the 8th International Conference on Learning and Intelligent Optimization , LNCS 8426, pages: 274-290, Lecture Notes in Computer Science, (Editors: Pardalos, PM., Resende, MGC., Vogiatzis, C., and Walteros, JL.), Springer, LION, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning Manipulation by Sequencing Motor Primitives with a Two-Armed Robot

Lioutikov, R., Kroemer, O., Peters, J., Maeda, G.

In Proceedings of the 13th International Conference on Intelligent Autonomous Systems, 302, pages: 1601-1611, Advances in Intelligent Systems and Computing, (Editors: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H.), Springer, IAS, 2014 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Kernel Mean Estimation via Spectral Filtering

Muandet, K., Sriperumbudur, B., Schölkopf, B.

In Advances in Neural Information Processing Systems 27, pages: 1-9, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Shaping Social Activity by Incentivizing Users

Farajtabar, M., Du, N., Gomez Rodriguez, M., Valera, I., Zha, H., Song, L.

In Advances in Neural Information Processing Systems 27, (Editors: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, ND., and Weinberger, KQ.), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Consistency of Causal Inference under the Additive Noise Model

Kpotufe, S., Sgouritsa, E., Janzing, D., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 478-495, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Active Learning of Linear Embeddings for Gaussian Processes

Garnett, R., Osborne, M., Hennig, P.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, pages: 230-239, (Editors: NL Zhang and J Tian), AUAI Press , Corvallis, Oregon, UAI2014, 2014, another link: http://arxiv.org/abs/1310.6740 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Decoding Index Finger Position from EEG Using Random Forests

Weichwald, S., Meyer, T., Schölkopf, B., Ball, T., Grosse-Wentrup, M.

In 4th International Workshop on Cognitive Information Processing (CIP), IEEE, CIP, 2014 (inproceedings)

PDF Arxiv DOI [BibTex]

PDF Arxiv DOI [BibTex]


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An Experimental Comparison of Bayesian Optimization for Bipedal Locomotion

Calandra, R., Seyfarth, A., Peters, J., Deisenroth, M.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 1951-1958, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Multi-Task Policy Search for Robotics

Deisenroth, M., Englert, P., Peters, J., Fox, D.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 3876-3881, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Sample-Based Information-Theoretic Stochastic Optimal Control

Lioutikov, R., Paraschos, A., Peters, J., Neumann, G.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 3896-3902, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers

Schober, M., Kasenburg, N., Feragen, A., Hennig, P., Hauberg, S.

In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Lecture Notes in Computer Science Vol. 8675, pages: 265-272, (Editors: P. Golland, N. Hata, C. Barillot, J. Hornegger and R. Howe), Springer, Heidelberg, MICCAI, 2014 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Estimating Causal Effects by Bounding Confounding

Geiger, P., Janzing, D., Schölkopf, B.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence , pages: 240-249 , (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon , UAI, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Re-ranking Approach to Classification in Large-scale Power-law Distributed Category Systems

Babbar, R., Partalas, I., Gaussier, E., Amini, M.

In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages: 1059-1062, (Editors: S Geva and A Trotman and P Bruza and CLA Clarke and K Järvelin), ACM, New York, NY, USA, SIGIR, 2014 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Kernel Mean Estimation and Stein Effect

Muandet, K., Fukumizu, K., Sriperumbudur, B., Gretton, A., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 10-18, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Active Reward Learning

Daniel, C., Viering, M., Metz, J., Kroemer, O., Peters, J.

In Proceedings of Robotics: Science & Systems, (Editors: Fox, D., Kavraki, LE., and Kurniawati, H.), RSS, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Multi-modal filtering for non-linear estimation

Kamthe, S., Peters, J., Deisenroth, M.

In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pages: 7979-7983, IEEE, ICASSP, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Inferring latent structures via information inequalities

Chaves, R., Luft, L., Maciel, T., Gross, D., Janzing, D., Schölkopf, B.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, pages: 112-121, (Editors: NL Zhang and J Tian), AUAI Press, Corvallis, Oregon, UAI, 2014 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Policy Search For Learning Robot Control Using Sparse Data

Bischoff, B., Nguyen-Tuong, D., van Hoof, H., McHutchon, A., Rasmussen, C., Knoll, A., Peters, J., Deisenroth, M.

In Proceedings of 2014 IEEE International Conference on Robotics and Automation, pages: 3882-3887, IEEE, ICRA, 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Learning to Unscrew a Light Bulb from Demonstrations

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

In Proceedings for the joint conference of ISR 2014, 45th International Symposium on Robotics and Robotik 2014, 2014 (inproceedings)

[BibTex]

[BibTex]


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Towards Neurofeedback Training of Associative Brain Areas for Stroke Rehabilitation

Özdenizci, O., Meyer, T., Cetin, M., Grosse-Wentrup, M.

In Proceedings of the 6th International Brain-Computer Interface Conference, (Editors: G Müller-Putz and G Bauernfeind and C Brunner and D Steyrl and S Wriessnegger and R Scherer), 2014 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

Gunter, T., Osborne, M., Garnett, R., Hennig, P., Roberts, S.

In Advances in Neural Information Processing Systems 27, pages: 2789-2797, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

Web link (url) [BibTex]

Web link (url) [BibTex]


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Scalable Kernel Methods via Doubly Stochastic Gradients

Dai, B., Xie, B., He, N., Liang, Y., Raj, A., Balcan, M., Song, L.

Advances in Neural Information Processing Systems 27, pages: 3041-3049, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Learning Economic Parameters from Revealed Preferences

Balcan, M., Daniely, A., Mehta, R., Urner, R., Vazirani, V. V.

In Web and Internet Economics - 10th International Conference, 8877, pages: 338-353, Lecture Notes in Computer Science, (Editors: Liu, T.-Y. and Qi, Q. and Ye, Y.), WINE, 2014 (inproceedings)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Fast Newton methods for the group fused lasso

Wytock, M., Sra, S., Kolter, J. Z.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, pages: 888-897, (Editors: Zhang, N. L. and Tian, J.), AUAI Press, UAI, 2014 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Mind the Gap: Subspace based Hierarchical Domain Adaptation

Raj, A., Namboodiri, V., Tuytelaars, T.

Transfer and Multi-task learning Workshop in Advances in Neural Information System Conference 27, 2014 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Localized Complexities for Transductive Learning

Tolstikhin, I., Blanchard, G., Kloft, M.

In Proceedings of the 27th Conference on Learning Theory, 35, pages: 857-884, (Editors: Balcan, M.-F. and Feldman, V. and Szepesvári, C.), JMLR, COLT, 2014 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Efficient Structured Matrix Rank Minimization

Yu, A. W., Ma, W., Yu, Y., Carbonell, J., Sra, S.

Advances in Neural Information Processing Systems 27, pages: 1350-1358, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Towards building a Crowd-Sourced Sky Map

Lang, D., Hogg, D., Schölkopf, B.

In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, JMLR W\&CP 33, pages: 549–557, (Editors: S. Kaski and J. Corander), JMLR.org, AISTATS, 2014 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Incremental Local Gaussian Regression

Meier, F., Hennig, P., Schaal, S.

In Advances in Neural Information Processing Systems 27, pages: 972-980, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014, clmc (inproceedings)

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Learning to Deblur

Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B.

In NIPS 2014 Deep Learning and Representation Learning Workshop, 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Efficient Bayesian Local Model Learning for Control

Meier, F., Hennig, P., Schaal, S.

In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pages: 2244 - 2249, IROS, 2014, clmc (inproceedings)

Abstract
Model-based control is essential for compliant controland force control in many modern complex robots, like humanoidor disaster robots. Due to many unknown and hard tomodel nonlinearities, analytical models of such robots are oftenonly very rough approximations. However, modern optimizationcontrollers frequently depend on reasonably accurate models,and degrade greatly in robustness and performance if modelerrors are too large. For a long time, machine learning hasbeen expected to provide automatic empirical model synthesis,yet so far, research has only generated feasibility studies butno learning algorithms that run reliably on complex robots.In this paper, we combine two promising worlds of regressiontechniques to generate a more powerful regression learningsystem. On the one hand, locally weighted regression techniquesare computationally efficient, but hard to tune due to avariety of data dependent meta-parameters. On the other hand,Bayesian regression has rather automatic and robust methods toset learning parameters, but becomes quickly computationallyinfeasible for big and high-dimensional data sets. By reducingthe complexity of Bayesian regression in the spirit of local modellearning through variational approximations, we arrive at anovel algorithm that is computationally efficient and easy toinitialize for robust learning. Evaluations on several datasetsdemonstrate very good learning performance and the potentialfor a general regression learning tool for robotics.

PDF link (url) DOI [BibTex]

PDF link (url) DOI [BibTex]


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The sample complexity of agnostic learning under deterministic labels

Ben-David, S., Urner, R.

In Proceedings of the 27th Conference on Learning Theory, 35, pages: 527-542, (Editors: Balcan, M.-F. and Feldman, V. and Szepesvári, C.), JMLR, COLT, 2014 (inproceedings)

link (url) [BibTex]

link (url) [BibTex]


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Towards an optimal stochastic alternating direction method of multipliers

Azadi, S., Sra, S.

Proceedings of the 31st International Conference on Machine Learning, 32, pages: 620-628, (Editors: Xing, E. P. and Jebara, T.), JMLR, ICML, 2014 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem

Gomez Rodriguez, M., Song, L., Schölkopf, B.

Proceedings of the 27th Conference on Learning Theory, 35, pages: 1276-1279, (Editors: Balcan, M.-F. and Szepesvári, C.), JMLR.org, COLT, 2014 (conference)

PDF [BibTex]

PDF [BibTex]


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Curiosity-driven learning with Context Tree Weighting

Peng, Z, Braun, DA

pages: 366-367, IEEE, Piscataway, NJ, USA, 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB), October 2014 (conference)

Abstract
In the first simulation, the intrinsic motivation of the agent was given by measuring learning progress through reduction in informational surprise (Figure 1 A-C). This way the agent should first learn the action that is easiest to learn (a1), and then switch to other actions that still allow for learning (a2) and ignore actions that cannot be learned at all (a3). This is exactly what we found in our simple environment. Compared to the original developmental learning algorithm based on learning progress proposed by Oudeyer [2], our Context Tree Weighting approach does not require local experts to do prediction, rather it learns the conditional probability distribution over observations given action in one structure. In the second simulation, the intrinsic motivation of the agent was given by measuring compression progress through improvement in compressibility (Figure 1 D-F). The agent behaves similarly: the agent first concentrates on the action with the most predictable consequence and then switches over to the regular action where the consequence is more difficult to predict, but still learnable. Unlike the previous simulation, random actions are also interesting to some extent because the compressed symbol strings use 8-bit representations, while only 2 bits are required for our observation space. Our preliminary results suggest that Context Tree Weighting might provide a useful representation to study problems of development.

DOI [BibTex]

DOI [BibTex]


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Monte Carlo methods for exact & efficient solution of the generalized optimality equations

Ortega, PA, Braun, DA, Tishby, N

pages: 4322-4327, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), June 2014 (conference)

Abstract
Previous work has shown that classical sequential decision making rules, including expectimax and minimax, are limit cases of a more general class of bounded rational planning problems that trade off the value and the complexity of the solution, as measured by its information divergence from a given reference. This allows modeling a range of novel planning problems having varying degrees of control due to resource constraints, risk-sensitivity, trust and model uncertainty. However, so far it has been unclear in what sense information constraints relate to the complexity of planning. In this paper, we introduce Monte Carlo methods to solve the generalized optimality equations in an efficient \& exact way when the inverse temperatures in a generalized decision tree are of the same sign. These methods highlight a fundamental relation between inverse temperatures and the number of Monte Carlo proposals. In particular, it is seen that the number of proposals is essentially independent of the size of the decision tree.

link (url) DOI [BibTex]

link (url) DOI [BibTex]

2013


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Falsification and future performance

Balduzzi, D.

In Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, 7070, pages: 65-78, Lecture Notes in Computer Science, Springer, Berlin, Germany, Solomonoff 85th Memorial Conference, January 2013 (inproceedings)

Abstract
We information-theoretically reformulate two measures of capacity from statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. We show these capacity measures count the number of hypotheses about a dataset that a learning algorithm falsifies when it finds the classifier in its repertoire minimizing empirical risk. It then follows from that the future performance of predictors on unseen data is controlled in part by how many hypotheses the learner falsifies. As a corollary we show that empirical VC-entropy quantifies the message length of the true hypothesis in the optimal code of a particular probability distribution, the so-called actual repertoire.

PDF Web DOI [BibTex]

2013

PDF Web DOI [BibTex]


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Feedback Error Learning for Rhythmic Motor Primitives

Gopalan, N., Deisenroth, M., Peters, J.

In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), pages: 1317-1322, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]