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2014


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Kernel methods in system identification, machine learning and function estimation: A survey

Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L.

Automatica, 50(3):657-682, 2014 (article)

ei

Web DOI [BibTex]

2014


Web DOI [BibTex]


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Development of a novel depth of interaction PET detector using highly multiplexed G-APD cross-strip encoding

Kolb, A., Parl, C., Mantlik, F., Liu, C., Lorenz, E., Renker, D., Pichler, B.

Medical Physics, 41(8), 2014 (article)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

Gunter, T., Osborne, M., Garnett, R., Hennig, P., Roberts, S.

In Advances in Neural Information Processing Systems 27, pages: 2789-2797, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

ei pn

Web link (url) [BibTex]

Web link (url) [BibTex]


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Scalable Kernel Methods via Doubly Stochastic Gradients

Dai, B., Xie, B., He, N., Liang, Y., Raj, A., Balcan, M., Song, L.

Advances in Neural Information Processing Systems 27, pages: 3041-3049, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Circular polarization interferometry: circularly polarized modes of cholesteric liquid crystals
Circular polarization interferometry: circularly polarized modes of cholesteric liquid crystals

Sanchez-Castillo, A., Eslami, S., Giesselmann, F., Fischer, P.

OPTICS EXPRESS, 22(25):31227-31236, 2014 (article)

Abstract
We describe a novel polarization interferometer which permits the determination of the refractive indices for circularly-polarized light. It is based on a Jamin-Lebedeff interferometer, modified with waveplates, and permits us to experimentally determine the refractive indices n(L) and n(R) of the respectively left- and right-circularly polarized modes in a cholesteric liquid crystal. Whereas optical rotation measurements only determine the circular birefringence, i.e. the difference (n(L) - n(R)), the interferometer also permits the determination of their absolute values. We report refractive indices of a cholesteric liquid crystal in the region of selective (Bragg) reflection as a function of temperature. (C) 2014 Optical Society of America

pf

DOI [BibTex]

DOI [BibTex]


Self-Propelling Nanomotors in the Presence of Strong Brownian Forces
Self-Propelling Nanomotors in the Presence of Strong Brownian Forces

Lee, T., Alarcon-Correa, M., Miksch, C., Hahn, K., Gibbs, J. G., Fischer, P.

NANO LETTERS, 14(5):2407-2412, 2014 (article)

Abstract
Motility in living systems is due to an array of complex molecular nanomotors that are essential for the function and survival of cells. These protein nanomotors operate not only despite of but also because of stochastic forces. Artificial means of realizing motility rely on local concentration or temperature gradients that are established across a particle, resulting in slip velocities at the particle surface and thus motion of the particle relative to the fluid. However, it remains unclear if these artificial motors can function at the smallest of scales, where Brownian motion dominates and no actively propelled living organisms can be found. Recently, the first reports have appeared suggesting that the swimming mechanisms of artificial structures may also apply to enzymes that are catalytically active. Here we report a scheme to realize artificial Janus nanoparticles (JNPs) with an overall size that is comparable to that of some enzymes similar to 30 nm. Our JNPs can catalyze the decomposition of hydrogen peroxide to water and oxygen and thus actively move by self-electrophoresis. Geometric anisotropy of the Pt-Au Janus nanoparticles permits the simultaneous observation of their translational and rotational motion by dynamic light scattering. While their dynamics is strongly influenced by Brownian rotation, the artificial Janus nanomotors show bursts of linear ballistic motion resulting in enhanced diffusion.

pf

DOI [BibTex]


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Epidural electrocorticography for monitoring of arousal in locked-in state

Martens, S., Bensch, M., Halder, S., Hill, J., Nijboer, F., Ramos-Murguialday, A., Schölkopf, B., Birbaumer, N., Gharabaghi, A.

Frontiers in Human Neuroscience, 8(861), 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Learning Economic Parameters from Revealed Preferences

Balcan, M., Daniely, A., Mehta, R., Urner, R., Vazirani, V. V.

In Web and Internet Economics - 10th International Conference, 8877, pages: 338-353, Lecture Notes in Computer Science, (Editors: Liu, T.-Y. and Qi, Q. and Ye, Y.), WINE, 2014 (inproceedings)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Fast Newton methods for the group fused lasso

Wytock, M., Sra, S., Kolter, J. Z.

In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, pages: 888-897, (Editors: Zhang, N. L. and Tian, J.), AUAI Press, UAI, 2014 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


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Simultaneous Whole-Body PET/MR Imaging in Comparison to PET/CT in Pediatric Oncology: Initial Results

Schäfer, J. F., Gatidis, S., Schmidt, H., Gückel, B., Bezrukov, I., Pfannenberg, C. A., Reimold, M., M., E., Fuchs, J., Claussen, C. D., Schwenzer, N. F.

Radiology, 273(1):220-231, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Mind the Gap: Subspace based Hierarchical Domain Adaptation

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

Transfer and Multi-task learning Workshop in Advances in Neural Information System Conference 27, 2014 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Shape control in wafer-based aperiodic 3D nanostructures
Shape control in wafer-based aperiodic 3D nanostructures

Hyeon-Ho, J., Mark, A. G., Gibbs, J. G., Reindl, T., Waizmann, U., Weis, J., Fischer, P.

NANOTECHNOLOGY, 25(23), 2014, Cover article. (article)

Abstract
Controlled local fabrication of three-dimensional (3D) nanostructures is important to explore and enhance the function of single nanodevices, but is experimentally challenging. We present a scheme based on e-beam lithography (EBL) written seeds, and glancing angle deposition (GLAD) grown structures to create nanoscale objects with defined shapes but in aperiodic arrangements. By using a continuous sacrificial corral surrounding the features of interest we grow isolated 3D nanostructures that have complex cross-sections and sidewall morphology that are surrounded by zones of clean substrate.

Cover article.

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DOI [BibTex]

DOI [BibTex]


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Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification

Persello, C., Boularias, A., Dalponte, M., Gobakken, T., Naesset, E., Schölkopf, B.

IEEE Transactions on Geoscience and Remote Sensing, 10(52):6652 - 6664, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Localized Complexities for Transductive Learning

Tolstikhin, I., Blanchard, G., Kloft, M.

In Proceedings of the 27th Conference on Learning Theory, 35, pages: 857-884, (Editors: Balcan, M.-F. and Feldman, V. and Szepesvári, C.), JMLR, COLT, 2014 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


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Principles of PET/MR Imaging

Disselhorst, J. A., Bezrukov, I., Kolb, A., Parl, C., Pichler, B. J.

Journal of Nuclear Medicine, 55(6, Supplement 2):2S-10S, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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IM3SHAPE: Maximum likelihood galaxy shear measurement code for cosmic gravitational lensing

Zuntz, J., Kacprzak, T., Voigt, L., Hirsch, M., Rowe, B., Bridle, S.

Astrophysics Source Code Library, 1, pages: 09013, 2014 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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Efficient nearest neighbors via robust sparse hashing

Cherian, A., Sra, S., Morellas, V., Papanikolopoulos, N.

IEEE Transactions on Image Processing, 23(8):3646-3655, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Efficient Structured Matrix Rank Minimization

Yu, A. W., Ma, W., Yu, Y., Carbonell, J., Sra, S.

Advances in Neural Information Processing Systems 27, pages: 1350-1358, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Towards building a Crowd-Sourced Sky Map

Lang, D., Hogg, D., Schölkopf, B.

In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, JMLR W\&CP 33, pages: 549–557, (Editors: S. Kaski and J. Corander), JMLR.org, AISTATS, 2014 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


Active Microrheology of the Vitreous of the Eye applied to Nanorobot Propulsion
Active Microrheology of the Vitreous of the Eye applied to Nanorobot Propulsion

Qiu, T., Schamel, D., Mark, A. G., Fischer, P.

In 2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), pages: 3801-3806, IEEE International Conference on Robotics and Automation ICRA, 2014, Best Automation Paper Award – Finalist. (inproceedings)

Abstract
Biomedical applications of micro or nanorobots require active movement through complex biological fluids. These are generally non-Newtonian (viscoelastic) fluids that are characterized by complicated networks of macromolecules that have size-dependent rheological properties. It has been suggested that an untethered microrobot could assist in retinal surgical procedures. To do this it must navigate the vitreous humor, a hydrated double network of collagen fibrils and high molecular-weight, polyanionic hyaluronan macromolecules. Here, we examine the characteristic size that potential robots must have to traverse vitreous relatively unhindered. We have constructed magnetic tweezers that provide a large gradient of up to 320 T/m to pull sub-micron paramagnetic beads through biological fluids. A novel two-step electrical discharge machining (EDM) approach is used to construct the tips of the magnetic tweezers with a resolution of 30 mu m and high aspect ratio of similar to 17:1 that restricts the magnetic field gradient to the plane of observation. We report measurements on porcine vitreous. In agreement with structural data and passive Brownian diffusion studies we find that the unhindered active propulsion through the eye calls for nanorobots with cross-sections of less than 500 nm.

Best Automation Paper Award – Finalist.

pf

[BibTex]

[BibTex]


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Incremental Local Gaussian Regression

Meier, F., Hennig, P., Schaal, S.

In Advances in Neural Information Processing Systems 27, pages: 972-980, (Editors: Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence and K.Q. Weinberger), 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014, clmc (inproceedings)

am ei pn

PDF link (url) [BibTex]

PDF link (url) [BibTex]


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Domain adaptation-can quantity compensate for quality?

Ben-David, S., Urner, R.

Annals of Mathematics and Artificial Intelligence, 70(3):185-202, 2014 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Sérsic galaxy models in weak lensing shape measurement: model bias, noise bias and their interaction

Kacprzak, T., Bridle, S., Rowe, B., Voigt, L., Zuntz, J., Hirsch, M., MacCrann, N.

Monthly Notices of the Royal Astronomical Society, 441(3):2528-2538, Oxford University Press, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Learning to Deblur

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

In NIPS 2014 Deep Learning and Representation Learning Workshop, 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


Swimming by reciprocal motion at low Reynolds number
Swimming by reciprocal motion at low Reynolds number

Qiu, T., Lee, T., Mark, A. G., Morozov, K. I., Muenster, R., Mierka, O., Turek, S., Leshansky, A. M., Fischer, P.

NATURE COMMUNICATIONS, 5, 2014, Max Planck Press Release. (article)

Abstract
Biological microorganisms swim with flagella and cilia that execute nonreciprocal motions for low Reynolds number (Re) propulsion in viscous fluids. This symmetry requirement is a consequence of Purcell's scallop theorem, which complicates the actuation scheme needed by microswimmers. However, most biomedically important fluids are non-Newtonian where the scallop theorem no longer holds. It should therefore be possible to realize a microswimmer that moves with reciprocal periodic body-shape changes in non-Newtonian fluids. Here we report a symmetric `micro-scallop', a single-hinge microswimmer that can propel in shear thickening and shear thinning (non-Newtonian) fluids by reciprocal motion at low Re. Excellent agreement between our measurements and both numerical and analytical theoretical predictions indicates that the net propulsion is caused by modulation of the fluid viscosity upon varying the shear rate. This reciprocal swimming mechanism opens new possibilities in designing biomedical microdevices that can propel by a simple actuation scheme in non-Newtonian biological fluids.

Max Planck Press Release.

pf

Video - A Swimming Micro-Scallop Video - Winner of the Micro-robotic Design Challenge in Hamlyn Symposium on Medical Robotics DOI [BibTex]

Video - A Swimming Micro-Scallop Video - Winner of the Micro-robotic Design Challenge in Hamlyn Symposium on Medical Robotics DOI [BibTex]


Nanohelices by shadow growth
Nanohelices by shadow growth

Gibbs, J. G., Mark, A. G., Lee, T., Eslami, S., Schamel, D., Fischer, P.

NANOSCALE, 6(16):9457-9466, 2014 (article)

Abstract
The helix has remarkable qualities and is prevalent in many fields including mathematics, physics, chemistry, and biology. This shape, which is chiral by nature, is ubiquitous in biology with perhaps the most famous example being DNA. Other naturally occurring helices are common at the nanoscale in the form of protein secondary structures and in various macromolecules. Nanoscale helices exhibit a wide range of interesting mechanical, optical, and electrical properties which can be intentionally engineered into the structure by choosing the correct morphology and material. As technology advances, these fabrication parameters can be fine-tuned and matched to the application of interest. Herein, we focus on the fabrication and properties of nanohelices grown by a dynamic shadowing growth method combined with fast wafer-scale substrate patterning which has a number of distinct advantages. We review the fabrication methodology and provide several examples that illustrate the generality and utility of nanohelices shadow-grown on nanopatterns.

pf

Video - Fabrication of Designer Nanostructures DOI [BibTex]


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Efficient Bayesian Local Model Learning for Control

Meier, F., Hennig, P., Schaal, S.

In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pages: 2244 - 2249, IROS, 2014, clmc (inproceedings)

Abstract
Model-based control is essential for compliant controland force control in many modern complex robots, like humanoidor disaster robots. Due to many unknown and hard tomodel nonlinearities, analytical models of such robots are oftenonly very rough approximations. However, modern optimizationcontrollers frequently depend on reasonably accurate models,and degrade greatly in robustness and performance if modelerrors are too large. For a long time, machine learning hasbeen expected to provide automatic empirical model synthesis,yet so far, research has only generated feasibility studies butno learning algorithms that run reliably on complex robots.In this paper, we combine two promising worlds of regressiontechniques to generate a more powerful regression learningsystem. On the one hand, locally weighted regression techniquesare computationally efficient, but hard to tune due to avariety of data dependent meta-parameters. On the other hand,Bayesian regression has rather automatic and robust methods toset learning parameters, but becomes quickly computationallyinfeasible for big and high-dimensional data sets. By reducingthe complexity of Bayesian regression in the spirit of local modellearning through variational approximations, we arrive at anovel algorithm that is computationally efficient and easy toinitialize for robust learning. Evaluations on several datasetsdemonstrate very good learning performance and the potentialfor a general regression learning tool for robotics.

am ei pn

PDF link (url) DOI [BibTex]

PDF link (url) DOI [BibTex]


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The sample complexity of agnostic learning under deterministic labels

Ben-David, S., Urner, R.

In Proceedings of the 27th Conference on Learning Theory, 35, pages: 527-542, (Editors: Balcan, M.-F. and Feldman, V. and Szepesvári, C.), JMLR, COLT, 2014 (inproceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


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Towards an optimal stochastic alternating direction method of multipliers

Azadi, S., Sra, S.

Proceedings of the 31st International Conference on Machine Learning, 32, pages: 620-628, (Editors: Xing, E. P. and Jebara, T.), JMLR, ICML, 2014 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Diminished White Matter Integrity in Patients with Systemic Lupus Erythematosus

Schmidt-Wilcke, T., Cagnoli, P., Wang, P., Schultz, T., Lotz, A., Mccune, W. J., Sundgren, P. C.

NeuroImage: Clinical, 5, pages: 291-297, 2014 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem

Gomez Rodriguez, M., Song, L., Schölkopf, B.

Proceedings of the 27th Conference on Learning Theory, 35, pages: 1276-1279, (Editors: Balcan, M.-F. and Szepesvári, C.), JMLR.org, COLT, 2014 (conference)

ei

PDF [BibTex]

PDF [BibTex]


Chiral Nanomagnets
Chiral Nanomagnets

Eslami, S., Gibbs, J. G., Rechkemmer, Y., van Slageren, J., Alarcon-Correa, M., Lee, T., Mark, A. G., Rikken, G. L. J. A., Fischer, P.

ACS PHOTONICS, 1(11):1231-1236, 2014 (article)

Abstract
We report on the enhanced optical properties of chiral magnetic nanohelices with critical dimensions comparable to the ferromagnetic domain size. They are shown to be ferromagnetic at room temperature, have defined chirality, and exhibit large optical activity in the visible as verified by electron microscopy, superconducting quantum interference device (SQUID) magnetometry, natural circular dichroism (NCD), and magnetic circular dichroism (MCD) measurements. The structures exhibit magneto-chiral dichroism (MChD), which directly demonstrates coupling between their structural chirality and magnetism. A chiral nickel (Ni) film consisting of an array of nanohelices similar to 100 nm in length exhibits an MChD anisotropy factor g(MChD) approximate to 10(-4) T-1 at room temperature in a saturation field of similar to 0.2 T, permitting polarization-independent control of the film's absorption properties through magnetic field modulation. This is also the first report of MChD in a material with structural chirality on the order of the wavelength of light, and therefore the Ni nanohelix array is a metamaterial with magnetochiral properties that can be tailored through a dynamic deposition process.

pf

DOI [BibTex]

DOI [BibTex]


Wireless powering of e-swimmers
Wireless powering of e-swimmers

Roche, J., Carrara, S., Sanchez, J., Lannelongue, J., Loget, G., Bouffier, L., Fischer, P., Kuhn, A.

SCIENTIFIC REPORTS, 4, 2014 (article)

Abstract
Miniaturized structures that can move in a controlled way in solution and integrate various functionalities are attracting considerable attention due to the potential applications in fields ranging from autonomous micromotors to roving sensors. Here we introduce a concept which allows, depending on their specific design, the controlled directional motion of objects in water, combined with electronic functionalities such as the emission of light, sensing, signal conversion, treatment and transmission. The approach is based on electric field-induced polarization, which triggers different chemical reactions at the surface of the object and thereby its propulsion. This results in a localized electric current that can power in a wireless way electronic devices in water, leading to a new class of electronic swimmers (e-swimmers).

pf

DOI [BibTex]

DOI [BibTex]


Swelling and shrinking behaviour of photoresponsive phosphonium-based ionogel microstructures
Swelling and shrinking behaviour of photoresponsive phosphonium-based ionogel microstructures

Czugala, M., O’Connell, C., Blin, C., Fischer, P., Fraser, K. J., Benito-Lopez, F., Diamond, D.

SENSORS AND ACTUATORS B-CHEMICAL, 194, pages: 105-113, 2014 (article)

Abstract
Photoresponsive N-isopropylacrylamide ionogel microstructures are presented in this study. These ionogels are synthesised using phosphonium based room temperature ionic liquids, together with the photochromic compound benzospiropyran. The microstructures can be actuated using light irradiation, facilitating non-contact and non-invasive operation. For the first time, the characterisation of the swelling and shrinking behaviour of several photopatterned ionogel microstructures is presented and the influence of surface-area-to-volume ratio on the swelling kinetics is evaluated. It was found that the swelling and shrinking behaviour of the ionogels is strongly dependent on the nature of the ionic liquid. In particular, the {[}P-6,P-6,P-6,P-14]{[}NTf2] ionogel exhibits the greatest degree of swelling, reaching up to 180\% of its initial size, and the fastest shrinkage rate (k(sh) = 29 +/- 4 x 10(-2) s(-1)). (C) 2014 Elsevier B. V. All rights reserved.

pf

DOI [BibTex]

DOI [BibTex]


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Self-Exploration of the Stumpy Robot with Predictive Information Maximization

Martius, G., Jahn, L., Hauser, H., V. Hafner, V.

In Proc. From Animals to Animats, SAB 2014, 8575, pages: 32-42, LNCS, Springer, 2014 (inproceedings)

al

[BibTex]

[BibTex]


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Information-Theoretic Bounded Rationality and ϵ-Optimality

Braun, DA, Ortega, PA

Entropy, 16(8):4662-4676, August 2014 (article)

Abstract
Bounded rationality concerns the study of decision makers with limited information processing resources. Previously, the free energy difference functional has been suggested to model bounded rational decision making, as it provides a natural trade-off between an energy or utility function that is to be optimized and information processing costs that are measured by entropic search costs. The main question of this article is how the information-theoretic free energy model relates to simple \(\epsilon\)-optimality models of bounded rational decision making, where the decision maker is satisfied with any action in an \(\epsilon\)-neighborhood of the optimal utility. We find that the stochastic policies that optimize the free energy trade-off comply with the notion of \(\epsilon\)-optimality. Moreover, this optimality criterion even holds when the environment is adversarial. We conclude that the study of bounded rationality based on \(\epsilon\)-optimality criteria that abstract away from the particulars of the information processing constraints is compatible with the information-theoretic free energy model of bounded rationality.

ei

DOI [BibTex]

DOI [BibTex]


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Occam’s Razor in sensorimotor learning

Genewein, T, Braun, D

Proceedings of the Royal Society of London B, 281(1783):1-7, May 2014 (article)

Abstract
A large number of recent studies suggest that the sensorimotor system uses probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam's Razor—an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here, we test directly for Occam's Razor in sensorimotor control. We designed a sensorimotor task in which participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models—a simple model with a large length scale, leading to smooth curves, and a complex model with a short length scale, leading to more wiggly curves. In training trials, participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials, participants were then exposed to ambiguous stimuli. In probe trials where the ambiguous stimulus could be fitted equally well by both models, we found that participants showed a clear preference for the simpler model. Moreover, we found that participants’ choice behaviour was quantitatively consistent with Bayesian Occam's Razor. We also show that participants’ drawn trajectories were similar to samples from the Bayesian predictive distribution over trajectories and significantly different from two non-probabilistic heuristics. In two control experiments, we show that the preference of the simpler model cannot be simply explained by a difference in physical effort or by a preference for curve smoothness. Our results suggest that Occam's Razor is a general behavioural principle already present during sensorimotor processing.

ei

DOI [BibTex]

DOI [BibTex]


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Generalized Thompson sampling for sequential decision-making and causal inference

Ortega, PA, Braun, DA

Complex Adaptive Systems Modeling, 2(2):1-23, March 2014 (article)

Abstract
Purpose Sampling an action according to the probability that the action is believed to be the optimal one is sometimes called Thompson sampling. Methods Although mostly applied to bandit problems, Thompson sampling can also be used to solve sequential adaptive control problems, when the optimal policy is known for each possible environment. The predictive distribution over actions can then be constructed by a Bayesian superposition of the policies weighted by their posterior probability of being optimal. Results Here we discuss two important features of this approach. First, we show in how far such generalized Thompson sampling can be regarded as an optimal strategy under limited information processing capabilities that constrain the sampling complexity of the decision-making process. Second, we show how such Thompson sampling can be extended to solve causal inference problems when interacting with an environment in a sequential fashion. Conclusion In summary, our results suggest that Thompson sampling might not merely be a useful heuristic, but a principled method to address problems of adaptive sequential decision-making and causal inference.

ei

DOI [BibTex]

DOI [BibTex]


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Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences

Peng, Z, Genewein, T, Braun, DA

Frontiers in Human Neuroscience, 8(168):1-13, March 2014 (article)

Abstract
Complexity is a hallmark of intelligent behavior consisting both of regular patterns and random variation. To quantitatively assess the complexity and randomness of human motion, we designed a motor task in which we translated subjects' motion trajectories into strings of symbol sequences. In the first part of the experiment participants were asked to perform self-paced movements to create repetitive patterns, copy pre-specified letter sequences, and generate random movements. To investigate whether the degree of randomness can be manipulated, in the second part of the experiment participants were asked to perform unpredictable movements in the context of a pursuit game, where they received feedback from an online Bayesian predictor guessing their next move. We analyzed symbol sequences representing subjects' motion trajectories with five common complexity measures: predictability, compressibility, approximate entropy, Lempel-Ziv complexity, as well as effective measure complexity. We found that subjects’ self-created patterns were the most complex, followed by drawing movements of letters and self-paced random motion. We also found that participants could change the randomness of their behavior depending on context and feedback. Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories.

ei

DOI [BibTex]

DOI [BibTex]


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Curiosity-driven learning with Context Tree Weighting

Peng, Z, Braun, DA

pages: 366-367, IEEE, Piscataway, NJ, USA, 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (IEEE ICDL-EPIROB), October 2014 (conference)

Abstract
In the first simulation, the intrinsic motivation of the agent was given by measuring learning progress through reduction in informational surprise (Figure 1 A-C). This way the agent should first learn the action that is easiest to learn (a1), and then switch to other actions that still allow for learning (a2) and ignore actions that cannot be learned at all (a3). This is exactly what we found in our simple environment. Compared to the original developmental learning algorithm based on learning progress proposed by Oudeyer [2], our Context Tree Weighting approach does not require local experts to do prediction, rather it learns the conditional probability distribution over observations given action in one structure. In the second simulation, the intrinsic motivation of the agent was given by measuring compression progress through improvement in compressibility (Figure 1 D-F). The agent behaves similarly: the agent first concentrates on the action with the most predictable consequence and then switches over to the regular action where the consequence is more difficult to predict, but still learnable. Unlike the previous simulation, random actions are also interesting to some extent because the compressed symbol strings use 8-bit representations, while only 2 bits are required for our observation space. Our preliminary results suggest that Context Tree Weighting might provide a useful representation to study problems of development.

ei

DOI [BibTex]

DOI [BibTex]


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Monte Carlo methods for exact & efficient solution of the generalized optimality equations

Ortega, PA, Braun, DA, Tishby, N

pages: 4322-4327, IEEE, Piscataway, NJ, USA, IEEE International Conference on Robotics and Automation (ICRA), June 2014 (conference)

Abstract
Previous work has shown that classical sequential decision making rules, including expectimax and minimax, are limit cases of a more general class of bounded rational planning problems that trade off the value and the complexity of the solution, as measured by its information divergence from a given reference. This allows modeling a range of novel planning problems having varying degrees of control due to resource constraints, risk-sensitivity, trust and model uncertainty. However, so far it has been unclear in what sense information constraints relate to the complexity of planning. In this paper, we introduce Monte Carlo methods to solve the generalized optimality equations in an efficient \& exact way when the inverse temperatures in a generalized decision tree are of the same sign. These methods highlight a fundamental relation between inverse temperatures and the number of Monte Carlo proposals. In particular, it is seen that the number of proposals is essentially independent of the size of the decision tree.

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]

2005


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Kernel Methods for Measuring Independence

Gretton, A., Herbrich, R., Smola, A., Bousquet, O., Schölkopf, B.

Journal of Machine Learning Research, 6, pages: 2075-2129, December 2005 (article)

Abstract
We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the covariance between functions of the random variables in reproducing kernel Hilbert spaces (RKHSs). We prove that when the RKHSs are universal, both functionals are zero if and only if the random variables are pairwise independent. We also show that the kernel mutual information is an upper bound near independence on the Parzen window estimate of the mutual information. Analogous results apply for two correlation-based dependence functionals introduced earlier: we show the kernel canonical correlation and the kernel generalised variance to be independence measures for universal kernels, and prove the latter to be an upper bound on the mutual information near independence. The performance of the kernel dependence functionals in measuring independence is verified in the context of independent component analysis.

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2005


PDF PostScript PDF [BibTex]


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Kernel ICA for Large Scale Problems

Jegelka, S., Gretton, A., Achlioptas, D.

In pages: -, NIPS Workshop on Large Scale Kernel Machines, December 2005 (inproceedings)

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Web [BibTex]

Web [BibTex]


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Infinite dimensional exponential families by reproducing kernel Hilbert spaces

Fukumizu, K.

In IGAIA 2005, pages: 324-333, 2nd International Symposium on Information Geometry and its Applications, December 2005 (inproceedings)

Abstract
The purpose of this paper is to propose a method of constructing exponential families of Hilbert manifold, on which estimation theory can be built. Although there have been works on infinite dimensional exponential families of Banach manifolds (Pistone and Sempi, 1995; Gibilisco and Pistone, 1998; Pistone and Rogantin, 1999), they are not appropriate to discuss statistical estimation with finite number of samples; the likelihood function with finite samples is not continuous on the manifold. In this paper we use a reproducing kernel Hilbert space as a functional space for constructing an exponential manifold. A reproducing kernel Hilbert space is dened as a Hilbert space of functions such that evaluation of a function at an arbitrary point is a continuous functional on the Hilbert space. Since we can discuss the value of a function with this space, it is very natural to use a manifold associated with a reproducing kernel Hilbert space as a basis of estimation theory. We focus on the maximum likelihood estimation (MLE) with the exponential manifold of a reproducing kernel Hilbert space. As in many non-parametric estimation methods, straightforward extension of MLE to an infinite dimensional exponential manifold suffers the problem of ill-posedness caused by the fact that the estimator should be chosen from the infinite dimensional space with only finite number of constraints given by the data. To solve this problem, a pseudo-maximum likelihood method is proposed by restricting the infinite dimensional manifold to a series of finite dimensional submanifolds, which enlarge as the number of samples increases. Some asymptotic results in the limit of infinite samples are shown, including the consistency of the pseudo-MLE.

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PDF Web [BibTex]

PDF Web [BibTex]


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A Unifying View of Sparse Approximate Gaussian Process Regression

Quinonero Candela, J., Rasmussen, C.

Journal of Machine Learning Research, 6, pages: 1935-1959, December 2005 (article)

Abstract
We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

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PDF [BibTex]

PDF [BibTex]


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Shortest-path kernels on graphs

Borgwardt, KM., Kriegel, H-P.

In pages: 74-81, IEEE Computer Society, Los Alamitos, CA, USA, Fifth International Conference on Data Mining (ICDM), November 2005 (inproceedings)

Abstract
Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available by defining a kernel function on instances of graphs. Graph kernels based on walks, subtrees and cycles in graphs have been proposed so far. As a general problem, these kernels are either computationally expensive or limited in their expressiveness. We try to overcome this problem by defining expressive graph kernels which are based on paths. As the computation of all paths and longest paths in a graph is NP-hard, we propose graph kernels based on shortest paths. These kernels are computable in polynomial time, retain expressivity and are still positive definite. In experiments on classification of graph models of proteins, our shortest-path kernels show significantly higher classification accuracy than walk-based kernels.

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Web DOI [BibTex]

Web DOI [BibTex]


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Training Support Vector Machines with Multiple Equality Constraints

Kienzle, W., Schölkopf, B.

In Proceedings of the 16th European Conference on Machine Learning, Lecture Notes in Computer Science, Vol. 3720, pages: 182-193, (Editors: JG Carbonell and J Siekmann), Springer, Berlin, Germany, ECML, November 2005 (inproceedings)

Abstract
In this paper we present a primal-dual decomposition algorithm for support vector machine training. As with existing methods that use very small working sets (such as Sequential Minimal Optimization (SMO), Successive Over-Relaxation (SOR) or the Kernel Adatron (KA)), our method scales well, is straightforward to implement, and does not require an external QP solver. Unlike SMO, SOR and KA, the method is applicable to a large number of SVM formulations regardless of the number of equality constraints involved. The effectiveness of our algorithm is demonstrated on a more difficult SVM variant in this respect, namely semi-parametric support vector regression.

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Measuring Statistical Dependence with Hilbert-Schmidt Norms

Gretton, A., Bousquet, O., Smola, A., Schoelkopf, B.

In Algorithmic Learning Theory, Lecture Notes in Computer Science, Vol. 3734, pages: 63-78, (Editors: S Jain and H-U Simon and E Tomita), Springer, Berlin, Germany, 16th International Conference ALT, October 2005 (inproceedings)

Abstract
We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on {methodname} do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

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PDF DOI [BibTex]

PDF DOI [BibTex]


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Maximal Margin Classification for Metric Spaces

Hein, M., Bousquet, O., Schölkopf, B.

Journal of Computer and System Sciences, 71(3):333-359, October 2005 (article)

Abstract
In order to apply the maximum margin method in arbitrary metric spaces, we suggest to embed the metric space into a Banach or Hilbert space and to perform linear classification in this space. We propose several embeddings and recall that an isometric embedding in a Banach space is always possible while an isometric embedding in a Hilbert space is only possible for certain metric spaces. As a result, we obtain a general maximum margin classification algorithm for arbitrary metric spaces (whose solution is approximated by an algorithm of Graepel. Interestingly enough, the embedding approach, when applied to a metric which can be embedded into a Hilbert space, yields the SVM algorithm, which emphasizes the fact that its solution depends on the metric and not on the kernel. Furthermore we give upper bounds of the capacity of the function classes corresponding to both embeddings in terms of Rademacher averages. Finally we compare the capacities of these function classes directly.

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PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]