Publications | Max Planck Institute for Intelligent Systems

2015

am ei Calandra, R., Ivaldi, S., Deisenroth, M., Peters, J. Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 690-695, Humanoids, November 2015 (inproceedings)

Evaluation of Interactive Object Recognition with Tactile Sensing

Hoelscher, J., Peters, J., Hermans, T.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 310-317, Humanoids, November 2015 (inproceedings)

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am ei Hoelscher, J., Peters, J., Hermans, T. Evaluation of Interactive Object Recognition with Tactile Sensing In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 310-317, Humanoids, November 2015 (inproceedings)

Optimizing Robot Striking Movement Primitives with Iterative Learning Control

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

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 80-87, Humanoids, November 2015 (inproceedings)

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am ei Koc, O., Maeda, G., Neumann, G., Peters, J. Optimizing Robot Striking Movement Primitives with Iterative Learning Control In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 80-87, Humanoids, November 2015 (inproceedings)

A Comparison of Contact Distribution Representations for Learning to Predict Object Interactions

Leischnig, S., Luettgen, S., Kroemer, O., Peters, J.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 616-622, Humanoids, November 2015 (inproceedings)

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am ei Leischnig, S., Luettgen, S., Kroemer, O., Peters, J. A Comparison of Contact Distribution Representations for Learning to Predict Object Interactions In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 616-622, Humanoids, November 2015 (inproceedings)

Quantifying changes in climate variability and extremes: Pitfalls and their overcoming

Sippel, S., Zscheischler, J., Heimann, M., Otto, F. E. L., Peters, J., Mahecha, M. D.

Geophysical Research Letters, 42(22):9990-9998, November 2015 (article)

ei Sippel, S., Zscheischler, J., Heimann, M., Otto, F. E. L., Peters, J., Mahecha, M. D. Quantifying changes in climate variability and extremes: Pitfalls and their overcoming Geophysical Research Letters, 42(22):9990-9998, November 2015 (article)

First-Person Tele-Operation of a Humanoid Robot

Fritsche, L., Unverzagt, F., Peters, J., Calandra, R.

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 997-1002, Humanoids, November 2015 (inproceedings)

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am ei Fritsche, L., Unverzagt, F., Peters, J., Calandra, R. First-Person Tele-Operation of a Humanoid Robot In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 997-1002, Humanoids, November 2015 (inproceedings)

Probabilistic Segmentation Applied to an Assembly Task

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

In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 533-540, Humanoids, November 2015 (inproceedings)

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am ei Lioutikov, R., Neumann, G., Maeda, G., Peters, J. Probabilistic Segmentation Applied to an Assembly Task In 15th IEEE-RAS International Conference on Humanoid Robots, pages: 533-540, Humanoids, November 2015 (inproceedings)

Diversity of sharp wave-ripple LFP signatures reveals differentiated brain-wide dynamical events

Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.

Proceedings of the National Academy of Sciences U.S.A, 112(46):E6379-E6387, November 2015 (article)

ei Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M. Diversity of sharp wave-ripple LFP signatures reveals differentiated brain-wide dynamical events Proceedings of the National Academy of Sciences U.S.A, 112(46):E6379-E6387, November 2015 (article)

Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results

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

Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

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. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.

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am ei ics pn Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S. Automatic LQR Tuning Based on Gaussian Process Optimization: Early Experimental Results Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

Diversity of sharp wave-ripples in the CA1 of the macaque hippocampus and their brain wide signatures

Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.

45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), October 2015 (poster)

ei Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M. Diversity of sharp wave-ripples in the CA1 of the macaque hippocampus and their brain wide signatures 45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), October 2015 (poster)

Noise masking of White’s illusion exposes the weakness of current spatial filtering models of lightness perception

Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M.

Journal of Vision, 15(14):1-17, October 2015 (article)

ei Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M. Noise masking of White’s illusion exposes the weakness of current spatial filtering models of lightness perception Journal of Vision, 15(14):1-17, October 2015 (article)

Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning

Tolstikhin, I., Zhivotovskiy, N., Blanchard, G.

In Proceedings of the 26th International Conference on Algorithmic Learning Theory, 9355, pages: 209-223, Lecture Notes in Computer Science, (Editors: K. Chaudhuri, C. Gentile and S. Zilles), Springer, ALT, October 2015 (inproceedings)

ei Tolstikhin, I., Zhivotovskiy, N., Blanchard, G. Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning In Proceedings of the 26th International Conference on Algorithmic Learning Theory, 9355, pages: 209-223, Lecture Notes in Computer Science, (Editors: K. Chaudhuri, C. Gentile and S. Zilles), Springer, ALT, October 2015 (inproceedings)

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)

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am ei Veiga, F., van Hoof, H., Peters, J., Hermans, T. Stabilizing Novel Objects by Learning to Predict Tactile Slip In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 5065-5072, IROS, September 2015 (inproceedings)

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer

Besserve, M., Lowe, S. C., Logothetis, N. K., Schölkopf, B., Panzeri, S.

PLOS Biology, 13(9):e1002257, September 2015 (article)

ei Besserve, M., Lowe, S. C., Logothetis, N. K., Schölkopf, B., Panzeri, S. Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer PLOS Biology, 13(9):e1002257, September 2015 (article)

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)

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am ei Paraschos, A., Rueckert, E., Peters, J., Neumann, G. Model-Free Probabilistic Movement Primitives for Physical Interaction In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 2860-2866, IROS, September 2015 (inproceedings)

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)

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am ei Wahrburg, A., Zeiss, S., Matthias, B., Peters, J., Ding, H. Combined Pose-Wrench and State Machine Representation for Modeling Robotic Assembly Skills In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 852-857, IROS, September 2015 (inproceedings)

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)

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am ei Manschitz, S., Kober, J., Gienger, M., Peters, J. Probabilistic Progress Prediction and Sequencing of Concurrent Movement Primitives In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 449-455, IROS, September 2015 (inproceedings)

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)

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am ei Parisi, S., Abdulsamad, H., Paraschos, A., Daniel, C., Peters, J. Reinforcement Learning vs Human Programming in Tetherball Robot Games In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 6428-6434, IROS, September 2015 (inproceedings)

Learning Motor Skills from Partially Observed Movements Executed at Different Speeds

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

In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 456-463, IROS, September 2015 (inproceedings)

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am ei Ewerton, M., Maeda, G., Peters, J., Neumann, G. Learning Motor Skills from Partially Observed Movements Executed at Different Speeds In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, pages: 456-463, IROS, September 2015 (inproceedings)

Janzing, D., Schölkopf, B.

Semi-Supervised Interpolation in an Anticausal Learning Scenario

Journal of Machine Learning Research, 16, pages: 1923-1948, September 2015 (article)

ei Janzing, D., Schölkopf, B. Semi-Supervised Interpolation in an Anticausal Learning Scenario Journal of Machine Learning Research, 16, pages: 1923-1948, September 2015 (article)

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

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

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

ei Ibarra Chaoul, A., Grosse-Wentrup, M. Is Breathing Rate a Confounding Variable in Brain-Computer Interfaces (BCIs) Based on EEG Spectral Power? Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages: 1079-1082, EMBC, August 2015 (conference)

Testing the role of luminance edges in White’s illusion with contour adaptation

Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M.

Journal of Vision, 15(11):1-16, August 2015 (article)

ei Betz, T., Shapley, R. M., Wichmann, F. A., Maertens, M. Testing the role of luminance edges in White’s illusion with contour adaptation Journal of Vision, 15(11):1-16, August 2015 (article)

Retrospective motion correction of magnitude-input MR images

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

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

ei Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B. Retrospective motion correction of magnitude-input MR images First International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), held in conjunction with ICML 2015, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, July 2015 (conference)

Direct Loss Minimization Inverse Optimal Control

Doerr, A., Ratliff, N., Bohg, J., Toussaint, M., Schaal, S.

In Proceedings of Robotics: Science and Systems, Rome, Italy, Robotics: Science and Systems XI, July 2015 (inproceedings)

Abstract

Inverse Optimal Control (IOC) has strongly impacted the systems engineering process, enabling automated planner tuning through straightforward and intuitive demonstration. The most successful and established applications, though, have been in lower dimensional problems such as navigation planning where exact optimal planning or control is feasible. In higher dimensional systems, such as humanoid robots, research has made substantial progress toward generalizing the ideas to model free or locally optimal settings, but these systems are complicated to the point where demonstration itself can be difficult. Typically, real-world applications are restricted to at best noisy or even partial or incomplete demonstrations that prove cumbersome in existing frameworks. This work derives a very flexible method of IOC based on a form of Structured Prediction known as Direct Loss Minimization. The resulting algorithm is essentially Policy Search on a reward function that rewards similarity to demonstrated behavior (using Covariance Matrix Adaptation (CMA) in our experiments). Our framework blurs the distinction between IOC, other forms of Imitation Learning, and Reinforcement Learning, enabling us to derive simple, versatile, and practical algorithms that blend imitation and reinforcement signals into a unified framework. Our experiments analyze various aspects of its performance and demonstrate its efficacy on conveying preferences for motion shaping and combined reach and grasp quality optimization.

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am ics Doerr, A., Ratliff, N., Bohg, J., Toussaint, M., Schaal, S. Direct Loss Minimization Inverse Optimal Control In Proceedings of Robotics: Science and Systems, Rome, Italy, Robotics: Science and Systems XI, July 2015 (inproceedings)

LMI-Based Synthesis for Distributed Event-Based State Estimation

Muehlebach, M., Trimpe, S.

In Proceedings of the American Control Conference, July 2015 (inproceedings)

Abstract

This paper presents an LMI-based synthesis procedure for distributed event-based state estimation. Multiple agents observe and control a dynamic process by sporadically exchanging data over a broadcast network according to an event-based protocol. In previous work [1], the synthesis of event-based state estimators is based on a centralized design. In that case three different types of communication are required: event-based communication of measurements, periodic reset of all estimates to their joint average, and communication of inputs. The proposed synthesis problem eliminates the communication of inputs as well as the periodic resets (under favorable circumstances) by accounting explicitly for the distributed structure of the control system.

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am ics Muehlebach, M., Trimpe, S. LMI-Based Synthesis for Distributed Event-Based State Estimation In Proceedings of the American Control Conference, July 2015 (inproceedings)

Guaranteed H2 Performance in Distributed Event-Based State Estimation

Muehlebach, M., Trimpe, S.

In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

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am ics Muehlebach, M., Trimpe, S. Guaranteed H2 Performance in Distributed Event-Based State Estimation In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

On the Choice of the Event Trigger in Event-based Estimation

Trimpe, S., Campi, M.

In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

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am ics Trimpe, S., Campi, M. On the Choice of the Event Trigger in Event-based Estimation In Proceeding of the First International Conference on Event-based Control, Communication, and Signal Processing, June 2015 (inproceedings)

Retrospective rigid motion correction of undersampled MRI data

Loktyushin, A., Babayeva, M., Gallichan, D., Krueger, G., Scheffler, K., Kober, T.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

ei Loktyushin, A., Babayeva, M., Gallichan, D., Krueger, G., Scheffler, K., Kober, T. Retrospective rigid motion correction of undersampled MRI data 23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

Improving Quantitative Susceptibility and R2* Mapping by Applying Retrospective Motion Correction

Feng, X., Loktyushin, A., Deistung, A., Reichenbach, J. R.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

ei Feng, X., Loktyushin, A., Deistung, A., Reichenbach, J. R. Improving Quantitative Susceptibility and R2* Mapping by Applying Retrospective Motion Correction 23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

Permutohedral Lattice CNNs

Kiefel, M., Jampani, V., Gehler, P. V.

In ICLR Workshop Track, ICLR, May 2015 (inproceedings)

Abstract

This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures.

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pdf link (url) [BibTex]

ei ps Kiefel, M., Jampani, V., Gehler, P. V. Permutohedral Lattice CNNs In ICLR Workshop Track, ICLR, May 2015 (inproceedings)

pdf link (url) [BibTex]

Leveraging Big Data for Grasp Planning

Kappler, D., Bohg, B., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract

We propose a new large-scale database containing grasps that are applied to a large set of objects from numerous categories. These grasps are generated in simulation and are annotated with different grasp stability metrics. We use a descriptive and efficient representation of the local object shape at which each grasp is applied. Given this data, we present a two-fold analysis: (i) We use crowdsourcing to analyze the correlation of the metrics with grasp success as predicted by humans. The results show that the metric based on physics simulation is a more consistent predictor for grasp success than the standard Îµ-metric. The results also support the hypothesis that human labels are not required for good ground truth grasp data. Instead the physics-metric can be used to generate datasets in simulation that may then be used to bootstrap learning in the real world. (ii) We apply a deep learning method and show that it can better leverage the large-scale database for prediction of grasp success compared to logistic regression. Furthermore, the results suggest that labels based on the physics-metric are less noisy than those from the Îµ-metric and therefore lead to a better classification performance.

PDF data DOI Project Page [BibTex]

am Kappler, D., Bohg, B., Schaal, S. Leveraging Big Data for Grasp Planning In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

PDF data DOI Project Page [BibTex]

Event-based Estimation and Control for Remote Robot Operation with Reduced Communication

Trimpe, S., Buchli, J.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract

An event-based communication framework for remote operation of a robot via a bandwidth-limited network is proposed. The robot sends state and environment estimation data to the operator, and the operator transmits updated control commands or policies to the robot. Event-based communication protocols are designed to ensure that data is transmitted only when required: the robot sends new estimation data only if this yields a significant information gain at the operator, and the operator transmits an updated control policy only if this comes with a significant improvement in control performance. The developed framework is modular and can be used with any standard estimation and control algorithms. Simulation results of a robotic arm highlight its potential for an efficient use of limited communication resources, for example, in disaster response scenarios such as the DARPA Robotics Challenge.

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am ics Trimpe, S., Buchli, J. Event-based Estimation and Control for Remote Robot Operation with Reduced Communication In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

arXiv Video Bayesian Filtering Framework Bayesian Object Tracking DOI Project Page [BibTex]

The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems

Wüthrich, M., Bohg, J., Kappler, D., Pfreundt, C., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

Abstract

Parametric filters, such as the Extended Kalman Filter and the Unscented Kalman Filter, typically scale well with the dimensionality of the problem, but they are known to fail if the posterior state distribution cannot be closely approximated by a density of the assumed parametric form. For nonparametric filters, such as the Particle Filter, the converse holds. Such methods are able to approximate any posterior, but the computational requirements scale exponentially with the number of dimensions of the state space. In this paper, we present the Coordinate Particle Filter which alleviates this problem. We propose to compute the particle weights recursively, dimension by dimension. This allows us to explore one dimension at a time, and resample after each dimension if necessary. Experimental results on simulated as well as real data con- firm that the proposed method has a substantial performance advantage over the Particle Filter in high-dimensional systems where not all dimensions are highly correlated. We demonstrate the benefits of the proposed method for the problem of multi-object and robotic manipulator tracking.

am Wüthrich, M., Bohg, J., Kappler, D., Pfreundt, C., Schaal, S. The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems In Proceedings of the IEEE International Conference on Robotics and Automation, May 2015 (inproceedings)

arXiv Video Bayesian Filtering Framework Bayesian Object Tracking DOI Project Page [BibTex]

Blind multirigid retrospective motion correction of MR images

Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.

Magnetic Resonance in Medicine, 73(4):1457-1468, April 2015 (article)

ei Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B. Blind multirigid retrospective motion correction of MR images Magnetic Resonance in Medicine, 73(4):1457-1468, April 2015 (article)

Understanding the Geometry of Workspace Obstacles in Motion Optimization

Ratliff, N., Toussaint, M., Schaal, S.

In Proceedings of the IEEE International Conference on Robotics and Automation, March 2015 (inproceedings)

am Ratliff, N., Toussaint, M., Schaal, S. Understanding the Geometry of Workspace Obstacles in Motion Optimization In Proceedings of the IEEE International Conference on Robotics and Automation, March 2015 (inproceedings)

A quantum advantage for inferring causal structure

Ried, K., Agnew, M., Vermeyden, L., Janzing, D., Spekkens, R. W., Resch, K. J.

Nature Physics, 11(5):414-420, March 2015 (article)

Abstract

The problem of inferring causal relations from observed correlations is relevant to a wide variety of scientific disciplines. Yet given the correlations between just two classical variables, it is impossible to determine whether they arose from a causal influence of one on the other or a common cause influencing both. Only a randomized trial can settle the issue. Here we consider the problem of causal inference for quantum variables. We show that the analogue of a randomized trial, causal tomography, yields a complete solution. We also show that, in contrast to the classical case, one can sometimes infer the causal structure from observations alone. We implement a quantum-optical experiment wherein we control the causal relation between two optical modes, and two measurement schemes—with and without randomization—that extract this relation from the observed correlations. Our results show that entanglement and quantum coherence provide an advantage for causal inference.

ei Ried, K., Agnew, M., Vermeyden, L., Janzing, D., Spekkens, R. W., Resch, K. J. A quantum advantage for inferring causal structure Nature Physics, 11(5):414-420, March 2015 (article)

Positive definite matrices and the S-divergence

Sra, S.

Proceedings of the American Mathematical Society, 2015, Published electronically: October 22, 2015 (article)

ei Sra, S. Positive definite matrices and the S-divergence Proceedings of the American Mathematical Society, 2015, Published electronically: October 22, 2015 (article)

Adaptive information-theoretic bounded rational decision-making with parametric priors

Grau-Moya, J, Braun, DA

pages: 1-4, NIPS Workshop on Bounded Optimality and Rational Metareasoning, December 2015 (conference)

Abstract

Deviations from rational decision-making due to limited computational resources have been studied in the field of bounded rationality, originally proposed by Herbert Simon. There have been a number of different approaches to model bounded rationality ranging from optimality principles to heuristics. Here we take an information-theoretic approach to bounded rationality, where information-processing costs are measured by the relative entropy between a posterior decision strategy and a given fixed prior strategy. In the case of multiple environments, it can be shown that there is an optimal prior rendering the bounded rationality problem equivalent to the rate distortion problem for lossy compression in information theory. Accordingly, the optimal prior and posterior strategies can be computed by the well-known Blahut-Arimoto algorithm which requires the computation of partition sums over all possible outcomes and cannot be applied straightforwardly to continuous problems. Here we derive a sampling-based alternative update rule for the adaptation of prior behaviors of decision-makers and we show convergence to the optimal prior predicted by rate distortion theory. Importantly, the update rule avoids typical infeasible operations such as the computation of partition sums. We show in simulations a proof of concept for discrete action and environment domains. This approach is not only interesting as a generic computational method, but might also provide a more realistic model of human decision-making processes occurring on a fast and a slow time scale.

ei Grau-Moya, J, Braun, DA Adaptive information-theoretic bounded rational decision-making with parametric priors pages: 1-4, NIPS Workshop on Bounded Optimality and Rational Metareasoning, December 2015 (conference)

Structural Intervention Distance (SID) for Evaluating Causal Graphs

Peters, J., Bühlmann, P.

Neural Computation , 27(3):771-799, 2015 (article)

ei Peters, J., Bühlmann, P. Structural Intervention Distance (SID) for Evaluating Causal Graphs Neural Computation , 27(3):771-799, 2015 (article)

Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence

Zhang, J., Zhang, K.

Philosophy of Science, Supplementary Volume 2015, 82(5):930-940, 2015 (article)

ei Zhang, J., Zhang, K. Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence Philosophy of Science, Supplementary Volume 2015, 82(5):930-940, 2015 (article)

Increasing the sensitivity of Kepler to Earth-like exoplanets

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

Workshop: 225th American Astronomical Society Meeting 2015 , pages: 105.01D, 2015 (poster)

Web link (url) [BibTex]

ei Foreman-Mackey, D., Hogg, D., Schölkopf, B., Wang, D. Increasing the sensitivity of Kepler to Earth-like exoplanets Workshop: 225th American Astronomical Society Meeting 2015 , pages: 105.01D, 2015 (poster)

Web link (url) [BibTex]

On the Intersection Property of Conditional Independence and its Application to Causal Discovery

Peters, J.

Journal of Causal Inference, 3(1):97-108, 2015 (article)

ei Peters, J. On the Intersection Property of Conditional Independence and its Application to Causal Discovery Journal of Causal Inference, 3(1):97-108, 2015 (article)

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

Küffner, R., Zach, N., Norel, R., Hawe, J., Schoenfeld, D., Wang, L., Li, G., Fang, L., Mackey, L., Hardiman, O., Cudkowicz, M., Sherman, A., Ertaylan, G., Grosse-Wentrup, M., Hothorn, T., van Ligtenberg, J., Macke, J., Meyer, T., Schölkopf, B., Tran, L., Vaughan, R., Stolovitzky, G., Leitner, M.

Nature Biotechnology, 33, pages: 51-57, 2015 (article)

ei Küffner, R., Zach, N., Norel, R., Hawe, J., Schoenfeld, D., Wang, L., Li, G., Fang, L., Mackey, L., Hardiman, O., Cudkowicz, M., Sherman, A., Ertaylan, G., Grosse-Wentrup, M., Hothorn, T., van Ligtenberg, J., Macke, J., Meyer, T., Schölkopf, B., Tran, L., Vaughan, R., Stolovitzky, G., Leitner, M. Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression Nature Biotechnology, 33, pages: 51-57, 2015 (article)

Inference of Cause and Effect with Unsupervised Inverse Regression

Sgouritsa, E., Janzing, D., Hennig, P., Schölkopf, B.

In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)

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Web PDF [BibTex]

ei pn Sgouritsa, E., Janzing, D., Hennig, P., Schölkopf, B. Inference of Cause and Effect with Unsupervised Inverse Regression In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)

Web PDF [BibTex]

Distinguishing Cause from Effect Based on Exogeneity

Zhang, K., Zhang, J., Schölkopf, B.

In Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge, pages: 261-271, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

ei Zhang, K., Zhang, J., Schölkopf, B. Distinguishing Cause from Effect Based on Exogeneity In Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge, pages: 261-271, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

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Hennig, P.

SIAM Journal on Optimization, 25(1):234-260, 2015 (article)

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Web PDF link (url) DOI [BibTex]

ei pn Hennig, P. Probabilistic Interpretation of Linear Solvers SIAM Journal on Optimization, 25(1):234-260, 2015 (article)

Web PDF link (url) DOI [BibTex]

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Mariti, C., Muscolo, G., Peters, J., Puig, D., Recchiuto, C., Sighieri, C., Solanas, A., von Stryk, O.

Journal of Veterinary Behavior: Clinical Applications and Research, 10(3):248-254, 2015 (article)

ei Mariti, C., Muscolo, G., Peters, J., Puig, D., Recchiuto, C., Sighieri, C., Solanas, A., von Stryk, O. Developing biorobotics for veterinary research into cat movements Journal of Veterinary Behavior: Clinical Applications and Research, 10(3):248-254, 2015 (article)

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In 24th International Joint Conference on Artificial Intelligence, Machine Learning Track, pages: 3561-3568, (Editors: Yang, Q. and Wooldridge, M.), AAAI Press, Palo Alto, California USA, IJCAI15, 2015 (inproceedings)

ei Huang, B., Zhang, K., Schölkopf, B. Identification of Time-Dependent Causal Model: A Gaussian Process Treatment In 24th International Joint Conference on Artificial Intelligence, Machine Learning Track, pages: 3561-3568, (Editors: Yang, Q. and Wooldridge, M.), AAAI Press, Palo Alto, California USA, IJCAI15, 2015 (inproceedings)