Publications

Empirical Inference Conference Paper Adaptive information-theoretic bounded rational decision-making with parametric priors Grau-Moya, J., Braun, D. 1-4, NIPS 2015 Workshop on Bounded Optimality and Rational Metareasoning, December 2015

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.

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Empirical Inference Article Entropic Movement Complexity Reflects Subjective Creativity Rankings of Visualized Hand Motion Trajectories Peng, Z., Braun, D. Frontiers in Psychology, 6(1879):1-13, December 2015

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In a previous study we have shown that human motion trajectories can be characterized by translating continuous trajectories into symbol sequences with well-defined complexity measures. Here we test the hypothesis that the motion complexity individuals generate in their movements might be correlated to the degree of creativity assigned by a human observer to the visualized motion trajectories. We asked participants to generate 55 novel hand movement patterns in virtual reality, where each pattern had to be repeated 10 times in a row to ensure reproducibility. This allowed us to estimate a probability distribution over trajectories for each pattern. We assessed motion complexity not only by the previously proposed complexity measures on symbolic sequences, but we also propose two novel complexity measures that can be directly applied to the distributions over trajectories based on the frameworks of Gaussian Processes and Probabilistic Movement Primitives. In contrast to previous studies, these new methods allow computing complexities of individual motion patterns from very few sample trajectories. We compared the different complexity measures to how a group of independent jurors rank ordered the recorded motion trajectories according to their personal creativity judgment. We found three entropic complexity measures that correlate significantly with human creativity judgment and discuss differences between the measures. We also test whether these complexity measures correlate with individual creativity in divergent thinking tasks, but do not find any consistent correlation. Our results suggest that entropic complexity measures of hand motion may reveal domain-specific individual differences in kinesthetic creativity.

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Empirical Inference Article What is epistemic value in free energy models of learning and acting? A bounded rationality perspective Ortega, P., Braun, D. Cognitive Neuroscience, 6(4):215-216, December 2015

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Free energy models of learning and acting do not only care about utility or extrinsic value, but also about intrinsic value, that is, the information value stemming from probability distributions that represent beliefs or strategies. While these intrinsic values can be interpreted as epistemic values or exploration bonuses under certain conditions, the framework of bounded rationality offers a complementary interpretation in terms of information-processing costs that we discuss here.

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Empirical Inference Autonomous Motion Conference Paper 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, 616-622, Humanoids, November 2015 (Published) DOI BibTeX

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper Evaluation of Interactive Object Recognition with Tactile Sensing Hoelscher, J., Peters, J., Hermans, T. In 15th IEEE-RAS International Conference on Humanoid Robots, 310-317, Humanoids, November 2015 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper 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, 997-1002, Humanoids, November 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin Calandra, R., Ivaldi, S., Deisenroth, M., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 690-695, Humanoids, November 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper 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, 80-87, Humanoids, November 2015 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper Probabilistic Segmentation Applied to an Assembly Task Lioutikov, R., Neumann, G., Maeda, G., Peters, J. In 15th IEEE-RAS International Conference on Humanoid Robots, 533-540, Humanoids, November 2015 (Published) DOI BibTeX

Empirical Inference Article 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 DOI BibTeX

Empirical Inference Ph.D. Thesis easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies Grimm, D. Eberhard Karls Universität Tübingen, November 2015 BibTeX

Empirical Inference Ph.D. Thesis Causal Discovery Beyond Conditional Independences Sgouritsa, E. Eberhard Karls Universität Tübingen, Germany, October 2015 (Published) URL BibTeX

Autonomous Motion Empirical Inference Probabilistic Numerics Intelligent Control Systems Conference Paper 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), Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (Published)

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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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Empirical Inference Article Bounded rationality, abstraction and hierarchical decision-making: an information-theoretic optimality principle Genewein, T., Leibfried, F., Grau-Moya, J., Braun, D. Frontiers in Robotics and AI, 2(27):1-24, October 2015

Abstract ›

Abstraction and hierarchical information-processing are hallmarks of human and animal intelligence underlying the unrivaled flexibility of behavior in biological systems. Achieving such a flexibility in artificial systems is challenging, even with more and more computational power. Here we investigate the hypothesis that abstraction and hierarchical information-processing might in fact be the consequence of limitations in information-processing power. In particular, we study an information-theoretic framework of bounded rational decision-making that trades off utility maximization against information-processing costs. We apply the basic principle of this framework to perception-action systems with multiple information-processing nodes and derive bounded optimal solutions. We show how the formation of abstractions and decision-making hierarchies depends on information-processing costs. We illustrate the theoretical ideas with example simulations and conclude by formalizing a mathematically unifying optimization principle that could potentially be extended to more complex systems.

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Empirical Inference Poster 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 (Published) URL BibTeX

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Conference Paper 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, 9355:209-223, Lecture Notes in Computer Science, (Editors: K. Chaudhuri, C. Gentile and S. Zilles), Springer, ALT, October 2015 (Published) DOI BibTeX

Empirical Inference Talk Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism Besserve, M. 53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (Published) BibTeX

Empirical Inference Ph.D. Thesis From Points to Probability Measures: A Statistical Learning on Distributions with Kernel Mean Embedding Muandet, K. University of Tübingen, Germany, University of Tübingen, Germany, September 2015 BibTeX

Empirical Inference Ph.D. Thesis Machine Learning Approaches to Image Deconvolution Schuler, C. University of Tübingen, Germany, University of Tübingen, Germany, September 2015 BibTeX

Empirical Inference Autonomous Motion Conference Paper 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, 852-857, IROS, September 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper 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, 456-463, IROS, September 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper 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, 2860-2866, IROS, September 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper 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, 449-455, IROS, September 2015 (Published) DOI URL BibTeX

Empirical Inference Autonomous Motion Conference Paper 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, 6428-6434, IROS, September 2015 (Published) DOI URL BibTeX

Empirical Inference Article Semi-Supervised Interpolation in an Anticausal Learning Scenario Janzing, D., Schölkopf, B. Journal of Machine Learning Research, 16:1923-1948, September 2015 (Published) URL BibTeX

Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Autonomous Motion Conference Paper 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, 5065-5072, IROS, September 2015 (Published) DOI URL BibTeX

Empirical Inference Conference Paper Developing neural networks with neurons competing for survival Peng, Z., Braun, D. 152-153, IEEE, Piscataway, NJ, USA, 5th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB 2015), August 2015

Abstract ›

We study developmental growth in a feedforward neural network model inspired by the survival principle in nature. Each neuron has to select its incoming connections in a way that allow it to fire, as neurons that are not able to fire over a period of time degenerate and die. In order to survive, neurons have to find reoccurring patterns in the activity of the neurons in the preceding layer, because each neuron requires more than one active input at any one time to have enough activation for firing. The sensory input at the lowest layer therefore provides the maximum amount of activation that all neurons compete for. The whole network grows dynamically over time depending on how many patterns can be found and how many neurons can maintain themselves accordingly. We show in simulations that this naturally leads to abstractions in higher layers that emerge in a unsupervised fashion. When evaluating the network in a supervised learning paradigm, it is clear that our network is not competitive. What is interesting though is that this performance was achieved by neurons that simply struggle for survival and do not know about performance error. In contrast to most studies on neural evolution that rely on a network-wide fitness function, our goal was to show that learning behaviour can appear in a system without being driven by any specific utility function or reward signal.

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

Empirical Inference Article Signaling equilibria in sensorimotor interactions Leibfried, F., Grau-Moya, J., Braun, D. Cognition, 141:73-86, August 2015

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Although complex forms of communication like human language are often assumed to have evolved out of more simple forms of sensorimotor signaling, less attention has been devoted to investigate the latter. Here, we study communicative sensorimotor behavior of humans in a two-person joint motor task where each player controls one dimension of a planar motion. We designed this joint task as a game where one player (the sender) possesses private information about a hidden target the other player (the receiver) wants to know about, and where the sender's actions are costly signals that influence the receiver's control strategy. We developed a game-theoretic model within the framework of signaling games to investigate whether subjects' behavior could be adequately described by the corresponding equilibrium solutions. The model predicts both separating and pooling equilibria, in which signaling does and does not occur respectively. We observed both kinds of equilibria in subjects and found that, in line with model predictions, the propensity of signaling decreased with increasing signaling costs and decreasing uncertainty on the part of the receiver. Our study demonstrates that signaling games, which have previously been applied to economic decision-making and animal communication, provide a framework for human signaling behavior arising during sensorimotor interactions in continuous and dynamic environments.

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Empirical Inference Article Structure Learning in Bayesian Sensorimotor Integration Genewein, T., Hez, E., Razzaghpanah, Z., Braun, D. PLoS Computational Biology, 11(8):1-27, August 2015

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Previous studies have shown that sensorimotor processing can often be described by Bayesian learning, in particular the integration of prior and feedback information depending on its degree of reliability. Here we test the hypothesis that the integration process itself can be tuned to the statistical structure of the environment. We exposed human participants to a reaching task in a three-dimensional virtual reality environment where we could displace the visual feedback of their hand position in a two dimensional plane. When introducing statistical structure between the two dimensions of the displacement, we found that over the course of several days participants adapted their feedback integration process in order to exploit this structure for performance improvement. In control experiments we found that this adaptation process critically depended on performance feedback and could not be induced by verbal instructions. Our results suggest that structural learning is an important meta-learning component of Bayesian sensorimotor integration.

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Empirical Inference Article 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 (Published) DOI BibTeX

Empirical Inference Article A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker Leibfried, F., Braun, D. Neural Computation, 27(8):1686-1720, July 2015

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Rate distortion theory describes how to communicate relevant information most efficiently over a channel with limited capacity. One of the many applications of rate distortion theory is bounded rational decision making, where decision makers are modeled as information channels that transform sensory input into motor output under the constraint that their channel capacity is limited. Such a bounded rational decision maker can be thought to optimize an objective function that trades off the decision maker's utility or cumulative reward against the information processing cost measured by the mutual information between sensory input and motor output. In this study, we interpret a spiking neuron as a bounded rational decision maker that aims to maximize its expected reward under the computational constraint that the mutual information between the neuron's input and output is upper bounded. This abstract computational constraint translates into a penalization of the deviation between the neuron's instantaneous and average firing behavior. We derive a synaptic weight update rule for such a rate distortion optimizing neuron and show in simulations that the neuron efficiently extracts reward-relevant information from the input by trading off its synaptic strengths against the collected reward.

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Empirical Inference Conference Paper 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, 9487:3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, July 2015 (Published) DOI BibTeX

Empirical Inference 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 (Published) BibTeX

Empirical Inference Book Chapter Kernel methods in medical imaging Charpiat, G., Hofmann, M., Schölkopf, B. In Handbook of Biomedical Imaging, 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (Published) Web URL BibTeX

Empirical Inference Poster 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 (Published) BibTeX

Perceiving Systems Empirical Inference Conference Paper Permutohedral Lattice CNNs Kiefel, M., Jampani, V., Gehler, P. V. In ICLR Workshop Track, ICLR, May 2015 (Published)

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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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Empirical Inference Ph.D. Thesis Blind Retrospective Motion Correction of MR Images Loktyushin, A. University of Tübingen, Germany, May 2015 BibTeX

Empirical Inference Talk Independence of cause and mechanism in brain networks Besserve, M. DALI workshop on Networks: Processes and Causality, April 2015 (Published) BibTeX

Empirical Inference Article 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 DOI BibTeX

Empirical Inference Article 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 (Published)

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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.

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Empirical Inference Article Positive definite matrices and the S-divergence Sra, S. Proceedings of the American Mathematical Society, 2015, Published electronically: October 22, 2015 (Published) DOI BibTeX

Empirical Inference Conference Paper A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis Hohmann, M. R., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Müller vom Hagen, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M. In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, 3187-3191, SMC, 2015 (Published) PDF DOI BibTeX

Empirical Inference Master Thesis A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis Hohmann, M. Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2015 BibTeX

Empirical Inference Autonomous Motion Conference Paper A Probabilistic Framework for Semi-Autonomous Robots Based on Interaction Primitives with Phase Estimation Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Peters, J. In Proceedings of the International Symposium of Robotics Research, ISRR, 2015 (Published) URL BibTeX

Empirical Inference Probabilistic Numerics Conference Paper A Random Riemannian Metric for Probabilistic Shortest-Path Tractography Hauberg, S., Schober, M., Liptrot, M., Hennig, P., Feragen, A. In 18th International Conference on Medical Image Computing and Computer Assisted Intervention, 9349:597-604, Lecture Notes in Computer Science, MICCAI, 2015 (Published) PDF DOI BibTeX

Empirical Inference Article A systematic search for transiting planets in the K2 data Foreman-Mackey, D., Montet, B., Hogg, D., Morton, T., Wang, D., Schölkopf, B. The Astrophysical Journal, 806(2), 2015 (Published)

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Photometry of stars from the K2 extension of NASA’s Kepler mission is afflicted by systematic effects caused by small (few-pixel) drifts in the telescope pointing and other spacecraft issues. We present a method for searching K2 light curves for evidence of exoplanets by simultaneously fitting for these systematics and the transit signals of interest. This method is more computationally expensive than standard search algorithms but we demonstrate that it can be efficiently implemented and used to discover transit signals. We apply this method to the full Campaign 1 data set and report a list of 36 planet candidates transiting 31 stars, along with an analysis of the pipeline performance and detection efficiency based on artificial signal injections and recoveries. For all planet candidates, we present posterior distributions on the properties of each system based strictly on the transit observables.

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