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2016


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Active Uncertainty Calibration in Bayesian ODE Solvers

Kersting, H., Hennig, P.

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

Abstract
There is resurging interest, in statistics and machine learning, in solvers for ordinary differential equations (ODEs) that return probability measures instead of point estimates. Recently, Conrad et al.~introduced a sampling-based class of methods that are `well-calibrated' in a specific sense. But the computational cost of these methods is significantly above that of classic methods. On the other hand, Schober et al.~pointed out a precise connection between classic Runge-Kutta ODE solvers and Gaussian filters, which gives only a rough probabilistic calibration, but at negligible cost overhead. By formulating the solution of ODEs as approximate inference in linear Gaussian SDEs, we investigate a range of probabilistic ODE solvers, that bridge the trade-off between computational cost and probabilistic calibration, and identify the inaccurate gradient measurement as the crucial source of uncertainty. We propose the novel filtering-based method Bayesian Quadrature filtering (BQF) which uses Bayesian quadrature to actively learn the imprecision in the gradient measurement by collecting multiple gradient evaluations.

ei pn

link (url) Project Page Project Page [BibTex]

2016


link (url) Project Page Project Page [BibTex]


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The Arrow of Time in Multivariate Time Serie

Bauer, S., Schölkopf, B., Peters, J.

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

ei

link (url) [BibTex]

link (url) [BibTex]


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A Kernel Test for Three-Variable Interactions with Random Processes

Rubenstein, P. K., Chwialkowski, K. P., Gretton, A.

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

ei

PDF Supplement Arxiv [BibTex]

PDF Supplement Arxiv [BibTex]


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Continuous Deep Q-Learning with Model-based Acceleration

Gu, S., Lillicrap, T., Sutskever, I., Levine, S.

Proceedings of the 33nd International Conference on Machine Learning (ICML), 48, pages: 2829-2838, JMLR Workshop and Conference Proceedings, (Editors: Maria-Florina Balcan and Kilian Q. Weinberger), JMLR.org, June 2016 (conference)

ei

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Bounded Rational Decision-Making in Feedforward Neural Networks

Leibfried, F, Braun, D

Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI), pages: 407-416, June 2016 (conference)

Abstract
Bounded rational decision-makers transform sensory input into motor output under limited computational resources. Mathematically, such decision-makers can be modeled as information-theoretic channels with limited transmission rate. Here, we apply this formalism for the first time to multilayer feedforward neural networks. We derive synaptic weight update rules for two scenarios, where either each neuron is considered as a bounded rational decision-maker or the network as a whole. In the update rules, bounded rationality translates into information-theoretically motivated types of regularization in weight space. In experiments on the MNIST benchmark classification task for handwritten digits, we show that such information-theoretic regularization successfully prevents overfitting across different architectures and attains results that are competitive with other recent techniques like dropout, dropconnect and Bayes by backprop, for both ordinary and convolutional neural networks.

ei

[BibTex]

[BibTex]


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Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles

Widmaier, F., Kappler, D., Schaal, S., Bohg, 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)

Abstract
To achieve accurate vision-based control with a robotic arm, a good hand-eye coordination is required. However, knowing the current configuration of the arm can be very difficult due to noisy readings from joint encoders or an inaccurate hand-eye calibration. We propose an approach for robot arm pose estimation that uses depth images of the arm as input to directly estimate angular joint positions. This is a frame-by-frame method which does not rely on good initialisation of the solution from the previous frames or knowledge from the joint encoders. For estimation, we employ a random regression forest which is trained on synthetically generated data. We compare different training objectives of the forest and also analyse the influence of prior segmentation of the arms on accuracy. We show that this approach improves previous work both in terms of computational complexity and accuracy. Despite being trained on synthetic data only, we demonstrate that the estimation also works on real depth images.

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Optimizing for what matters: the Top Grasp Hypothesis

Kappler, D., Schaal, S., Bohg, 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)

Abstract
In this paper, we consider the problem of robotic grasping of objects when only partial and noisy sensor data of the environment is available. We are specifically interested in the problem of reliably selecting the best hypothesis from a whole set. This is commonly the case when trying to grasp an object for which we can only observe a partial point cloud from one viewpoint through noisy sensors. There will be many possible ways to successfully grasp this object, and even more which will fail. We propose a supervised learning method that is trained with a ranking loss. This explicitly encourages that the top-ranked training grasp in a hypothesis set is also positively labeled. We show how we adapt the standard ranking loss to work with data that has binary labels and explain the benefits of this formulation. Additionally, we show how we can efficiently optimize this loss with stochastic gradient descent. In quantitative experiments, we show that we can outperform previous models by a large margin.

am

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Exemplar-based Prediction of Object Properties from Local Shape Similarity

Bohg, J., Kappler, D., Schaal, S.

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

Abstract
We propose a novel method that enables a robot to identify a graspable object part of an unknown object given only noisy and partial information that is obtained from an RGB-D camera. Our method combines the benefits of local with the advantages of global methods. It learns a classifier that takes a local shape representation as input and outputs the probability that a grasp applied at this location will be successful. Given a query data point that is classified in this way, we can retrieve all the locally similar training data points and use them to predict latent global object shape. This information may help to further prune positively labeled grasp hypotheses based on, e.g. relation to the predicted average global shape or suitability for a specific task. This prediction can also guide scene exploration to prune object shape hypotheses. To learn the function that maps local shape to grasp stability we use a Random Forest Classifier. We show that our method reaches the same classification performance as the current state-of-the-art on this dataset which uses a Convolutional Neural Network. Additionally, we exploit the natural ability of the Random Forest to cluster similar data. For a positively predicted query data point, we retrieve all the locally similar training data points that are associated with the same leaf nodes of the Random Forest. The main insight from this work is that local object shape that affords a grasp is also a good predictor of global object shape. We empirically support this claim with quantitative experiments. Additionally, we demonstrate the predictive capability of the method on some real data examples.

am

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


Thumb xl screen shot 2016 01 19 at 14.48.37
Automatic LQR Tuning Based on Gaussian Process Global Optimization

Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages: 270-277, IEEE, IEEE International Conference on Robotics and Automation, May 2016 (inproceedings)

Abstract
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree- of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of a two- and four- dimensional tuning problems highlight the method’s potential for automatic controller tuning on robotic platforms.

am ics pn

Video PDF DOI Project Page [BibTex]

Video PDF DOI Project Page [BibTex]


Thumb xl screen shot 2016 01 19 at 14.56.20
Depth-based Object Tracking Using a Robust Gaussian Filter

Issac, J., Wüthrich, M., Garcia Cifuentes, C., Bohg, J., Trimpe, S., Schaal, S.

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

Abstract
We consider the problem of model-based 3D- tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard Gaussian filter to this problem. First of all, depth sensors are characterized by fat-tailed measurement noise. To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand. Thereby, we avoid using heuristic outlier detection methods that simply reject measurements if they do not match the model. Secondly, the computational cost of the standard Gaussian filter is prohibitive due to the high-dimensional measurement, i.e. the depth image. To address this problem, we propose an approximation to reduce the computational complexity of the filter. In quantitative experiments on real data we show how our method clearly outperforms the standard Gaussian filter. Furthermore, we compare its performance to a particle-filter-based tracking method, and observe comparable computational efficiency and improved accuracy and smoothness of the estimates.

am ics

Video Bayesian Object Tracking Library Bayesian Filtering Framework Object Tracking Dataset link (url) DOI Project Page [BibTex]

Video Bayesian Object Tracking Library Bayesian Filtering Framework Object Tracking Dataset link (url) DOI Project Page [BibTex]


Thumb xl screen shot 2016 06 27 at 09.38.59
Implications of Action-Oriented Paradigm Shifts in Cognitive Science

Dominey, P. F., Prescott, T. J., Bohg, J., Engel, A. K., Gallagher, S., Heed, T., Hoffmann, M., Knoblich, G., Prinz, W., Schwartz, A.

In The Pragmatic Turn - Toward Action-Oriented Views in Cognitive Science, 18, pages: 333-356, 20, Strüngmann Forum Reports, vol. 18, J. Lupp, series editor, (Editors: Andreas K. Engel and Karl J. Friston and Danica Kragic), The MIT Press, 18th Ernst Strüngmann Forum, May 2016 (incollection) In press

Abstract
An action-oriented perspective changes the role of an individual from a passive observer to an actively engaged agent interacting in a closed loop with the world as well as with others. Cognition exists to serve action within a landscape that contains both. This chapter surveys this landscape and addresses the status of the pragmatic turn. Its potential influence on science and the study of cognition are considered (including perception, social cognition, social interaction, sensorimotor entrainment, and language acquisition) and its impact on how neuroscience is studied is also investigated (with the notion that brains do not passively build models, but instead support the guidance of action). A review of its implications in robotics and engineering includes a discussion of the application of enactive control principles to couple action and perception in robotics as well as the conceptualization of system design in a more holistic, less modular manner. Practical applications that can impact the human condition are reviewed (e.g. educational applications, treatment possibilities for developmental and psychopathological disorders, the development of neural prostheses). All of this foreshadows the potential societal implications of the pragmatic turn. The chapter concludes that an action-oriented approach emphasizes a continuum of interaction between technical aspects of cognitive systems and robotics, biology, psychology, the social sciences, and the humanities, where the individual is part of a grounded cultural system.

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The Pragmatic Turn - Toward Action-Oriented Views in Cognitive Science 18th Ernst Strüngmann Forum Bibliography Chapter link (url) [BibTex]

The Pragmatic Turn - Toward Action-Oriented Views in Cognitive Science 18th Ernst Strüngmann Forum Bibliography Chapter link (url) [BibTex]


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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), 51, pages: 648-657, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C.), May 2016 (conference)

ei pn

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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MuProp: Unbiased Backpropagation for Stochastic Neural Networks

Gu, S., Levine, S., Sutskever, I., Mnih, A.

4th International Conference on Learning Representations (ICLR), May 2016 (conference)

ei

Arxiv [BibTex]

Arxiv [BibTex]


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Communication Rate Analysis for Event-based State Estimation

(Best student paper finalist)

Ebner, S., Trimpe, S.

In Proceedings of the 13th International Workshop on Discrete Event Systems, May 2016 (inproceedings)

am ics

PDF DOI [BibTex]

PDF DOI [BibTex]


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

Hohmann, M. R., 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, May 2016 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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Autofocusing-based correction of B0 fluctuation-induced ghosting

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

24th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2016 (poster)

ei

link (url) [BibTex]

link (url) [BibTex]


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Movement Primitives with Multiple Phase Parameters

Ewerton, M., Maeda, G., Neumann, G., Kisner, V., Kollegger, G., Wiemeyer, J., Peters, J.

IEEE International Conference on Robotics and Automation (ICRA), pages: 201-206, IEEE, May 2016 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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TerseSVM : A Scalable Approach for Learning Compact Models in Large-scale Classification

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

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

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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A Lightweight Robotic Arm with Pneumatic Muscles for Robot Learning

Büchler, D., Ott, H., Peters, J.

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)

am ei

ICRA16final DOI Project Page [BibTex]

ICRA16final DOI Project Page [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), 51, pages: 676-684, JMLR Workshop and Conference Proceedings, (Editors: Gretton, A. and Robert, C. C. ), May 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.

ei pn

link (url) Project Page Project Page [BibTex]

link (url) Project Page Project Page [BibTex]


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Drifting Gaussian Processes with Varying Neighborhood Sizes for Online Model Learning

Meier, F., Schaal, S.

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

am

[BibTex]

[BibTex]


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Learning Action-Perception Cycles in Robotics: A Question of Representations and Embodiment

Bohg, J., Kragic, D.

In The Pragmatic Turn - Toward Action-Oriented Views in Cognitive Science, 18, pages: 309-320, 18, Strüngmann Forum Reports, vol. 18, J. Lupp, series editor, (Editors: Andreas K. Engel and Karl J. Friston and Danica Kragic), The MIT Press, 18th Ernst Strüngmann Forum, May 2016 (incollection) In press

Abstract
Since the 1950s, robotics research has sought to build a general-purpose agent capable of autonomous, open-ended interaction with realistic, unconstrained environments. Cognition is perceived to be at the core of this process, yet understanding has been challenged because cognition is referred to differently within and across research areas, and is not clearly defined. The classic robotics approach is decomposition into functional modules which perform planning, reasoning, and problem-solving or provide input to these mechanisms. Although advancements have been made and numerous success stories reported in specific niches, this systems-engineering approach has not succeeded in building such a cognitive agent. The emergence of an action-oriented paradigm offers a new approach: action and perception are no longer separable into functional modules but must be considered in a complete loop. This chapter reviews work on different mechanisms for action- perception learning and discusses the role of embodiment in the design of the underlying representations and learning. It discusses the evaluation of agents and suggests the development of a new embodied Turing Test. Appropriate scenarios need to be devised in addition to current competitions, so that abilities can be tested over long time periods.

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18th Ernst Strüngmann Forum The Pragmatic Turn- Toward Action-Oriented Views in Cognitive Science Bibliography Chapter link (url) [BibTex]

18th Ernst Strüngmann Forum The Pragmatic Turn- Toward Action-Oriented Views in Cognitive Science Bibliography Chapter link (url) [BibTex]


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Learning soft task priorities for control of redundant robots

Modugno, V., Neumann, G., Rueckert, E., Oriolo, G., Peters, J., Ivaldi, S.

IEEE International Conference on Robotics and Automation (ICRA), pages: 221-226, IEEE, May 2016 (conference)

ei

DOI [BibTex]

DOI [BibTex]


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On the Reliability of Information and Trustworthiness of Web Sources in Wikipedia

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

Wikipedia workshop at the 10th International AAAI Conference on Web and Social Media (ICWSM), May 2016 (conference)

ei

[BibTex]

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

ei

Arxiv Peer-Grading dataset request [BibTex]

Arxiv Peer-Grading dataset request [BibTex]


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Distinct adaptation to abrupt and gradual torque perturbations with a multi-joint exoskeleton robot

Oh, Y., Sutanto, G., Mistry, M., Schweighofer, N., Schaal, S.

Abstracts of Neural Control of Movement Conference (NCM 2016), Montego Bay, Jamaica, April 2016 (poster)

am

[BibTex]

[BibTex]


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On estimation of functional causal models: General results and application to post-nonlinear causal model

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

ACM Transactions on Intelligent Systems and Technologies, 7(2):article no. 13, January 2016 (article)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Fabular: Regression Formulas As Probabilistic Programming

Borgström, J., Gordon, A. D., Ouyang, L., Russo, C., Ścibior, A., Szymczak, M.

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

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Special Issue on Causal Discovery and Inference

Zhang, K., Li, J., Bareinboim, E., Schölkopf, B., Pearl, J.

ACM Transactions on Intelligent Systems and Technology (TIST), 7(2), January 2016, (Guest Editors) (misc)

ei

[BibTex]

[BibTex]


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Supplemental material for ’Communication Rate Analysis for Event-based State Estimation’

Ebner, S., Trimpe, S.

Max Planck Institute for Intelligent Systems, January 2016 (techreport)

am ics

PDF [BibTex]

PDF [BibTex]


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Gaussian Process-Based Predictive Control for Periodic Error Correction

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

IEEE Transactions on Control Systems Technology , 24(1):110-121, 2016 (article)

ei pn

PDF DOI [BibTex]

PDF DOI [BibTex]


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Empirical Inference (2010-2015)
Scientific Advisory Board Report, 2016 (misc)

ei

pdf [BibTex]

pdf [BibTex]


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Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Townsend, J., Koep, N., Weichwald, S.

Journal of Machine Learning Research, 17(137):1-5, 2016 (article)

ei

PDF Arxiv Code Project page link (url) [BibTex]


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

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

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

ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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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 ECML-PKDD 2016 (article)

am ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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PGO wave-triggered functional MRI: mapping the networks underlying synaptic consolidation

Logothetis, N. K., Murayama, Y., Ramirez-Villegas, J. F., Besserve, M., Evrard, H.

47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)

ei

[BibTex]

[BibTex]


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Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set

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

2016 (misc)

ei

Arxiv [BibTex]

Arxiv [BibTex]


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Influence of initial fixation position in scene viewing

Rothkegel, L. O. M., Trukenbrod, H. A., Schütt, H. H., Wichmann, F. A., Engbert, R.

Vision Research, 129, pages: 33-49, 2016 (article)

ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Testing models of peripheral encoding using metamerism in an oddity paradigm

Wallis, T. S. A., Bethge, M., Wichmann, F. A.

Journal of Vision, 16(2), 2016 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Modeling Confounding by Half-Sibling Regression

Schölkopf, B., Hogg, D., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C. J., Peters, J.

Proceedings of the National Academy of Science, 113(27):7391-7398, 2016 (article)

ei

Code link (url) DOI Project Page [BibTex]

Code link (url) DOI Project Page [BibTex]


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Dual Control for Approximate Bayesian Reinforcement Learning

Klenske, E. D., Hennig, P.

Journal of Machine Learning Research, 17(127):1-30, 2016 (article)

ei pn

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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A Population Based Gaussian Mixture Model Incorporating 18F-FDG-PET and DW-MRI Quantifies Tumor Tissue Classes

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

Journal of Nuclear Medicine, 57(3):473-479, 2016 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Nonlinear functional causal models for distinguishing cause from effect

Zhang, K., Hyvärinen, A.

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

[BibTex]

[BibTex]


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Ensuring Ethical Behavior from Autonomous Systems

Anderson, M., Anderson, S. L., Berenz, V.

In Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Papers from the 2016 AAAI Workshop, Phoenix, Arizona, USA, February 12, 2016, 2016 (inproceedings)

am

link (url) [BibTex]

link (url) [BibTex]


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Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data

Schütt, H. H., Harmeling, S., Macke, J. H., Wichmann, F. A.

Vision Research, 122, pages: 105-123, 2016 (article)

ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]