624 results (BibTeX)

2017


Electroencephalographic identifiers of motor adaptation learning

Ozdenizci, O., Yalcin, M., Erdogan, A., Patoglu, V., Grosse-Wentrup, M., Cetin, M.

Journal of Neural Engineering, 2017 (article) Submitted

[BibTex]

2017

[BibTex]


BundleMAP: Anatomically Localized Classification, Regression, and Hypothesis Testing in Diffusion MRI

Khatami, M., Schmidt-Wilcke, T., Sundgren, P., Abbasloo, A., Schölkopf, B., Schultz, T.

Pattern Recognition, 63, pages: 593-600, 2017 (article)

DOI [BibTex]

DOI [BibTex]


Model Selection for Gaussian Mixture Models

Huang, T., Peng, H., Zhang, K.

Statistica Sinica, 27(1):147-169, 2017 (article)

link (url) [BibTex]

link (url) [BibTex]


Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills

Kupcsik, A., Deisenroth, M., Peters, J., Ai Poh, L., Vadakkepat, V., Neumann, G.

Artificial Intelligence, 247, pages: 415-439, 2017, Special Issue on AI and Robotics (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Anticipatory Action Selection for Human-Robot Table Tennis

Wang, Z., Boularias, A., Mülling, K., Schölkopf, B., Peters, J.

Artificial Intelligence, 247, pages: 399-414, 2017, Special Issue on AI and Robotics (article)

Abstract
Abstract Anticipation can enhance the capability of a robot in its interaction with humans, where the robot predicts the humans' intention for selecting its own action. We present a novel framework of anticipatory action selection for human-robot interaction, which is capable to handle nonlinear and stochastic human behaviors such as table tennis strokes and allows the robot to choose the optimal action based on prediction of the human partner's intention with uncertainty. The presented framework is generic and can be used in many human-robot interaction scenarios, for example, in navigation and human-robot co-manipulation. In this article, we conduct a case study on human-robot table tennis. Due to the limited amount of time for executing hitting movements, a robot usually needs to initiate its hitting movement before the opponent hits the ball, which requires the robot to be anticipatory based on visual observation of the opponent's movement. Previous work on Intention-Driven Dynamics Models (IDDM) allowed the robot to predict the intended target of the opponent. In this article, we address the problem of action selection and optimal timing for initiating a chosen action by formulating the anticipatory action selection as a Partially Observable Markov Decision Process (POMDP), where the transition and observation are modeled by the \{IDDM\} framework. We present two approaches to anticipatory action selection based on the \{POMDP\} formulation, i.e., a model-free policy learning method based on Least-Squares Policy Iteration (LSPI) that employs the \{IDDM\} for belief updates, and a model-based Monte-Carlo Planning (MCP) method, which benefits from the transition and observation model by the IDDM. Experimental results using real data in a simulated environment show the importance of anticipatory action selection, and that \{POMDPs\} are suitable to formulate the anticipatory action selection problem by taking into account the uncertainties in prediction. We also show that existing algorithms for POMDPs, such as \{LSPI\} and MCP, can be applied to substantially improve the robot's performance in its interaction with humans.

DOI [BibTex]

DOI [BibTex]

2016


Screening Rules for Convex Problems

Raj, A., Olbrich, J., Gärtner, B., Schölkopf, B., Jaggi, M.

2016 (article) Submitted

[BibTex]

2016

[BibTex]


Spectral Clustering predicts tumor tissue heterogeneity using dynamic 18F-FDG PET: a complement to the standard compartmental modeling approach

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

Journal of Nuclear Medicine, 2016, (published ahead of print November 3, 2016) (article)

DOI [BibTex]

DOI [BibTex]


A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

Katiyar, P., Divine, M., Kohlhofer, U., Quintanilla-Martinez, L., Schölkopf, B., Disselhorst, J.

Molecular Imaging and Biology, pages: 1-7, 2016 (article)

DOI [BibTex]

DOI [BibTex]


easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies

Grimm, D., Roqueiro, D., Salome, P., Kleeberger, S., Greshake, B., Zhu, W., Liu, C., Lippert, C., Stegle, O., Schölkopf, B., Weigel, D., Borgwardt, K.

The Plant Cell, 2016 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS

Fomina, T., Lohmann, G., Erb, M., Ethofer, T., Schölkopf, B., Grosse-Wentrup, M.

Journal of Neural Engineering, 13(6):066021, 2016 (article)

link (url) [BibTex]

link (url) [BibTex]


The population of long-period transiting exoplanets

Foreman-Mackey, D., Morton, T., Hogg, D., Agol, E., Schölkopf, B.

The Astrophysical Journal, 152(6):206, 2016 (article)

link (url) [BibTex]

link (url) [BibTex]


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 ECMLPKDD 2016 (article)

DOI [BibTex]

DOI [BibTex]


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

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

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

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Causal discovery and inference: concepts and recent methodological advances

Spirtes, P., Zhang, K.

Applied Informatics, 3(3):1-28, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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)

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


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

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

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

DOI [BibTex]

DOI [BibTex]


Causal inference using invariant prediction: identification and confidence intervals

Peters, J., Bühlmann, P., Meinshausen, N.

Journal of the Royal Statistical Society, Series B (Statistical Methodology), 78(5):947-1012, 2016, (with discussion) (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


MERLiN: Mixture Effect Recovery in Linear Networks

Weichwald, S., Grosse-Wentrup, M., Gretton, A.

IEEE Journal of Selected Topics in Signal Processing, 10(7):1254-1266, 2016 (article)

Arxiv Code PDF DOI [BibTex]

Arxiv Code PDF DOI [BibTex]


Transfer Learning in Brain-Computer Interfaces

Jayaram, V., Alamgir, M., Altun, Y., Schölkopf, B., Grosse-Wentrup, M.

IEEE Computational Intelligence Magazine, 11(1):20-31, 2016 (article)

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


Learning to Deblur

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

IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (article)

DOI [BibTex]

DOI [BibTex]


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Dual Control for Approximate Bayesian Reinforcement Learning

Klenske, E., Hennig, P.

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

PDF link (url) Project Page [BibTex]


Learning Taxonomy Adaptation in Large-scale Classification

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

Journal of Machine Learning Research, 17(98):1-37, 2016 (article)

link (url) [BibTex]

link (url) [BibTex]


Kernel Mean Shrinkage Estimators

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

Journal of Machine Learning Research, 17(48):1-41, 2016 (article)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Modeling Confounding by Half-Sibling Regression

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

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

Code link (url) DOI [BibTex]

Code link (url) DOI [BibTex]


Thumb md cloud tracking
Gaussian Process Based Predictive Control for Periodic Error Correction

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

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

PDF DOI Project Page [BibTex]


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), January 2016 (article)

PDF DOI [BibTex]

PDF DOI [BibTex]

2015


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

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

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

DOI [BibTex]

2015

DOI [BibTex]


A quantum advantage for inferring causal structure

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

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.

DOI [BibTex]

DOI [BibTex]


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

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

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

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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

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

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

DOI Project Page [BibTex]


Semi-Supervised Interpolation in an Anticausal Learning Scenario

Janzing, D., Schölkopf, B.

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

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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

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

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

DOI Project Page [BibTex]

DOI Project Page [BibTex]


Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data

Melchior, P., Suchyta, E., Huff, E., Hirsch, M., Kacprzak, T., Rykoff, E., Gruen, D., Armstrong, R., Bacon, D., Bechtol, K., others, O.

Monthly Notices of the Royal Astronomical Society, 449(3):2219-2238, Oxford University Press, 2015 (article)

DOI [BibTex]

DOI [BibTex]


Quantitative evaluation of segmentation- and atlas- based attenuation correction for PET/MR on pediatric patients

Bezrukov, I., Schmidt, H., Gatidis, S., Mantlik, F., Schäfer, J., Schwenzer, N., Pichler, B.

Journal of Nuclear Medicine, 56(7):1067-1074, 2015 (article)

DOI [BibTex]

DOI [BibTex]


Assessment of murine brain tissue shrinkage caused by different histological fixatives using magnetic resonance and computed tomography imaging

Wehrl, H., Bezrukov, I., Wiehr, S., Lehnhoff, M., Fuchs, K., Mannheim, J., Quintanilla-Martinez, L., Kneilling, M., Pichler, B., Sauter, A.

Histology and Histopathology, 30(5):601-613, 2015 (article)

[BibTex]

[BibTex]


Correlation matrix nearness and completion under observation uncertainty

Alaíz, C., Dinuzzo, F., Sra, S.

IMA Journal of Numerical Analysis, 35(1):325-340, 2015 (article)

DOI [BibTex]

DOI [BibTex]


Bayesian Optimization for Learning Gaits under Uncertainty

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

Annals of Mathematics and Artificial Intelligence, pages: 1-19, 2015 (article)

DOI [BibTex]

DOI [BibTex]


Learning Movement Primitive Attractor Goals and Sequential Skills from Kinesthetic Demonstrations

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

Robotics and Autonomous Systems, 74, Part A, pages: 97-107, 2015 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Active Reward Learning with a Novel Acquisition Function

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

Autonomous Robots, 39(3):389-405, 2015 (article)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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

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

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

DOI Project Page [BibTex]


Probabilistic numerics and uncertainty in computations

Hennig, P., Osborne, M., Girolami, M.

Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 471(2179), 2015 (article)

Abstract
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.

PDF DOI Project Page [BibTex]

PDF DOI Project Page [BibTex]


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 (article)

Abstract
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.

link (url) DOI [BibTex]

link (url) DOI [BibTex]


The Randomized Causation Coefficient

Lopez-Paz, D., Muandet, K., Recht, B.

Journal of Machine Learning, 16, pages: 2901-2907, 2015 (article)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Context affects lightness at the level of surfaces

Maertens, M., Wichmann, F., Shapley, R.

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

Web PDF link (url) DOI [BibTex]

Web PDF link (url) DOI [BibTex]


Spatial statistics and attentional dynamics in scene viewing

Engbert, R., Trukenbrod, H., Barthelmé, S., Wichmann, F.

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

Web PDF link (url) DOI Project Page [BibTex]

Web PDF link (url) DOI Project Page [BibTex]