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2014


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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]

2014

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]


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Gaussian Process Vine Copulas for Multivariate Dependence

Lopez-Paz, D., Hernandez-Lobato, J., Ghahramani, Z.

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28(2), pages: 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Poster: http://people.tuebingen.mpg.de/dlopez/papers/icml2013_gpvine_poster.pdf (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs)

Grosse-Wentrup, M., Schölkopf, B.

In Brain-Computer Interface Research, pages: 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 (inbook)

PDF DOI [BibTex]

PDF DOI [BibTex]


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The Randomized Dependence Coefficient

Lopez-Paz, D., Hennig, P., Schölkopf, B.

In Advances in Neural Information Processing Systems 26, pages: 1-9, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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On a link between kernel mean maps and Fraunhofer diffraction, with an application to super-resolution beyond the diffraction limit

Harmeling, S., Hirsch, M., Schölkopf, B.

In IEEE Conference on Computer Vision and Pattern Recognition, pages: 1083-1090, IEEE, CVPR, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Output Kernel Learning Methods

Dinuzzo, F., Ong, C., Fukumizu, K.

In International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: theory and applications, ROKS, 2013 (inproceedings)

[BibTex]

[BibTex]


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Alignment-based Transfer Learning for Robot Models

Bocsi, B., Csato, L., Peters, J.

In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN 2013), pages: 1-7, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method

Chen, Z., Zhang, K., Chan, L.

In 13th International Conference on Data Mining, pages: 1003-1008, (Editors: H. Xiong, G. Karypis, B. M. Thuraisingham, D. J. Cook and X. Wu), IEEE Computer Society, ICDM, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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A probabilistic approach to robot trajectory generation

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

In Proceedings of the 13th IEEE International Conference on Humanoid Robots (HUMANOIDS), pages: 477-483, IEEE, 13th IEEE-RAS International Conference on Humanoid Robots, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Geometric optimisation on positive definite matrices for elliptically contoured distributions

Sra, S., Hosseini, R.

In Advances in Neural Information Processing Systems 26, pages: 2562-2570, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Fast Probabilistic Optimization from Noisy Gradients

Hennig, P.

In Proceedings of The 30th International Conference on Machine Learning, JMLR W&CP 28(1), pages: 62–70, (Editors: S Dasgupta and D McAllester), ICML, 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Structure and Dynamics of Information Pathways in On-line Media

Gomez Rodriguez, M., Leskovec, J., Schölkopf, B.

In 6th ACM International Conference on Web Search and Data Mining (WSDM), pages: 23-32, (Editors: S Leonardi, A Panconesi, P Ferragina, and A Gionis), ACM, WSDM, 2013 (inproceedings)

Web DOI [BibTex]

Web DOI [BibTex]


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Evaluation and Analysis of the Performance of the EXP3 Algorithm in Stochastic Environments

Seldin, Y., Szepesvári, C., Auer, P., Abbasi-Yadkori, Y.

In Proceedings of the Tenth European Workshop on Reinforcement Learning , pages: 103-116, (Editors: MP Deisenroth and C Szepesvári and J Peters), JMLR, EWRL, 2013 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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Domain adaptation under Target and Conditional Shift

Zhang, K., Schölkopf, B., Muandet, K., Wang, Z.

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3), pages: 819–827, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

PDF Web [BibTex]

PDF Web [BibTex]


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From Ordinary Differential Equations to Structural Causal Models: the deterministic case

Mooij, J., Janzing, D., Schölkopf, B.

In Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence, pages: 440-448, (Editors: A Nicholson and P Smyth), AUAI Press, Corvallis, Oregon, UAI, 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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A machine learning approach for non-blind image deconvolution

Schuler, C., Burger, H., Harmeling, S., Schölkopf, B.

In IEEE Conference on Computer Vision and Pattern Recognition, pages: 1067-1074, IEEE, CVPR, 2013 (inproceedings)

Web Web DOI [BibTex]

Web Web DOI [BibTex]


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Autonomous Reinforcement Learning with Hierarchical REPS

Daniel, C., Neumann, G., Peters, J.

In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN 2013), pages: 1-8, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Geometric Tree Kernels: Classification of COPD from Airway Tree Geometry

Feragen, A., Petersen, J., Grimm, D., Dirksen, A., Pedersen, JH., Borgwardt, KM., de Bruijne, M.

In Information Processing in Medical Imaging, pages: 171-183, (Editors: JC Gee and S Joshi and KM Pohl and WM Wells and L Zöllei), Springer, Berlin Heidelberg, 23rd International Conference on Information Processing in Medical Imaging (IPMI), 2013, Lecture Notes in Computer Science, Vol. 7017 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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On estimation of functional causal models: Post-nonlinear causal model as an example

Zhang, K., Wang, Z., Schölkopf, B.

In First IEEE ICDM workshop on causal discovery , 2013, Held in conjunction with the 12th IEEE International Conference on Data Mining (ICDM 2013) (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Object Modeling and Segmentation by Robot Interaction with Cluttered Environments

van Hoof, H., Krömer, O., Peters, J.

In Proceedings of the IEEE International Conference on Humanoid Robots (HUMANOIDS), pages: 169-176, IEEE, 13th IEEE-RAS International Conference on Humanoid Robots, 2013 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Reflection methods for user-friendly submodular optimization

Jegelka, S., Bach, F., Sra, S.

In Advances in Neural Information Processing Systems 26, pages: 1313-1321, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Semi-supervised learning in causal and anticausal settings

Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J.

In Empirical Inference, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

DOI [BibTex]

DOI [BibTex]


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On Flat versus Hierarchical Classification in Large-Scale Taxonomies

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

In Advances in Neural Information Processing Systems 26, pages: 1824-1832, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Tractable large-scale optimization in machine learning

Sra, S.

In Tractability: Practical Approaches to Hard Problems, pages: 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (inbook)

[BibTex]

[BibTex]


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Data-Efficient Generalization of Robot Skills with Contextual Policy Search

Kupcsik, A., Deisenroth, M., Peters, J., Neumann, G.

In Proceedings of the 27th National Conference on Artificial Intelligence (AAAI 2013), (Editors: desJardins, M. and Littman, M. L.), AAAI Press, 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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One-class Support Measure Machines for Group Anomaly Detection

Muandet, K., Schölkopf, B.

In Proceedings 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 449-458, (Editors: Ann Nicholson and Padhraic Smyth), AUAI Press, Corvallis, Oregon, UAI, 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Modeling Information Propagation with Survival Theory

Gomez Rodriguez, M., Leskovec, J., Schölkopf, B.

In Proceedings of the 30th International Conference on Machine Learning, JMLR W&CP 28 (3), pages: 666-674, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

Web [BibTex]

Web [BibTex]


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How to Test the Quality of Reconstructed Sources in Independent Component Analysis (ICA) of EEG/MEG Data

Grosse-Wentrup, M., Harmeling, S., Zander, T., Hill, J., Schölkopf, B.

In Proceedings of the 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI), pages: 102-105, IEEE Xplore Digital Library, PRNI, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

Sgouritsa, E., Janzing, D., Peters, J., Schölkopf, B.

In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 556-565, (Editors: A Nicholson and P Smyth), AUAI Press Corvallis, Oregon, USA, UAI, 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Improving alpha matting and motion blurred foreground estimation

Köhler, R., Hirsch, M., Schölkopf, B., Harmeling, S.

In IEEE Conference on Image Processing (ICIP), pages: 3446-3450, IEEE, ICIP, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Towards Robot Skill Learning: From Simple Skills to Table Tennis

Peters, J., Kober, J., Mülling, K., Kroemer, O., Neumann, G.

In Machine Learning and Knowledge Discovery in Databases, Proceedings of the European Conference on Machine Learning, Part III (ECML 2013), LNCS 8190, pages: 627-631, (Editors: Blockeel, H.,Kersting, K., Nijssen, S., and Zelezný, F.), Springer, 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Nonparametric dynamics estimation for time periodic systems

Klenske, E., Zeilinger, M., Schölkopf, B., Hennig, P.

In Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing, pages: 486-493 , 2013 (inproceedings)

PDF DOI [BibTex]

PDF DOI [BibTex]


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Scalable kernels for graphs with continuous attributes

Feragen, A., Kasenburg, N., Petersen, J., de Bruijne, M., Borgwardt, KM.

In Advances in Neural Information Processing Systems 26, pages: 216-224, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Auto-Calibrating Spherical Deconvolution Based on ODF Sparsity

Schultz, T., Gröschel, S.

In Proceedings of Medical Image Computing and Computer-Assisted Intervention, Part I, pages: 663-670, (Editors: K Mori and I Sakuma and Y Sato and C Barillot and N Navab), Springer, MICCAI, 2013, Lecture Notes in Computer Science, vol. 8149 (inproceedings)

DOI [BibTex]

DOI [BibTex]


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Maximum-Margin Framework for Training Data Synchronization in Large-Scale Hierarchical Classification

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

In Neural Information Processing - 20th International Conference, Proceedings, Part I, Lecture Notes in Computer Science, Vol. 8226, pages: 336-343, (Editors: M Lee and A Hirose and Z-G Hou and R M Kil), Springer, ICONIP, 2013 (inproceedings)

Web [BibTex]

Web [BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K., Balduzzi, D., Schölkopf, B.

In Proceedings of the 30th International Conference on Machine Learning, W&CP 28(1), pages: 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Volume 28, number 1 (inproceedings)

Web [BibTex]

Web [BibTex]


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Learning Sequential Motor Tasks

Daniel, C., Neumann, G., Peters, J.

In Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA 2013), 2013 (inproceedings)

PDF [BibTex]

PDF [BibTex]


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Information-Theoretic Motor Skill Learning

Neumann, G., Kupcsik, A., Deisenroth, M., Peters, J.

In Proceedings of the 27th AAAI 2013, Workshop on Intelligent Robotic Systems (AAAI 2013), 2013 (inproceedings)

[BibTex]

[BibTex]


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Measuring Statistical Dependence via the Mutual Information Dimension

Sugiyama, M., Borgwardt, KM.

In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), pages: 1692-1698, (Editors: Francesca Rossi), AAAI Press, Menlo Park, California, IJCAI, 2013 (inproceedings)

[BibTex]

[BibTex]


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Analytical probabilistic proton dose calculation and range uncertainties

Bangert, M., Hennig, P., Oelfke, U.

In 17th International Conference on the Use of Computers in Radiation Therapy, pages: 6-11, (Editors: A. Haworth and T. Kron), ICCR, 2013 (inproceedings)

[BibTex]

[BibTex]