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2017


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Editorial for the Special Issue on Microdevices and Microsystems for Cell Manipulation

Hu, W., Ohta, A. T.

8, Multidisciplinary Digital Publishing Institute, September 2017 (misc)

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DOI [BibTex]

2017


DOI [BibTex]


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Robot Learning

Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J., Schaal, S.

In Springer Handbook of Robotics, pages: 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (inbook)

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Project Page [BibTex]

Project Page [BibTex]


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Policy Gradient Methods

Peters, J., Bagnell, J.

In Encyclopedia of Machine Learning and Data Mining, pages: 982-985, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Unsupervised clustering of EOG as a viable substitute for optical eye-tracking

Flad, N., Fomina, T., Bülthoff, H. H., Chuang, L. L.

In First Workshop on Eye Tracking and Visualization (ETVIS 2015), pages: 151-167, Mathematics and Visualization, (Editors: Burch, M., Chuang, L., Fisher, B., Schmidt, A., and Weiskopf, D.), Springer, 2017 (inbook)

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DOI [BibTex]

DOI [BibTex]


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Statistical Asymmetries Between Cause and Effect

Janzing, D.

In Time in Physics, pages: 129-139, Tutorials, Schools, and Workshops in the Mathematical Sciences, (Editors: Renner, Renato and Stupar, Sandra), Springer International Publishing, Cham, 2017 (inbook)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Robot Learning

Peters, J., Tedrake, R., Roy, N., Morimoto, J.

In Encyclopedia of Machine Learning and Data Mining, pages: 1106-1109, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (inbook)

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DOI Project Page [BibTex]

DOI Project Page [BibTex]

2012


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Methods, apparatuses, and systems for micromanipulation with adhesive fibrillar structures

Sitti, M., Mengüç, Y.

December 2012, US Patent App. 14/368,079 (misc)

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[BibTex]

2012



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Dry adhesive structures

Sitti, M., Murphy, M., Aksak, B.

December 2012, US Patent App. 13/533,386 (misc)

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[BibTex]

[BibTex]


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Methods of making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

June 2012, US Patent 8,206,631 (misc)

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[BibTex]

[BibTex]


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Dry adhesives and methods for making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

March 2012, US Patent App. 13/429,621 (misc)

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[BibTex]

[BibTex]


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Expectation-Maximization methods for solving (PO)MDPs and optimal control problems

Toussaint, M., Storkey, A., Harmeling, S.

In Inference and Learning in Dynamic Models, (Editors: Barber, D., Cemgil, A.T. and Chiappa, S.), Cambridge University Press, Cambridge, UK, January 2012 (inbook) In press

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PDF [BibTex]

PDF [BibTex]


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Inferential structure determination from NMR data

Habeck, M.

In Bayesian methods in structural bioinformatics, pages: 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 (inbook)

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[BibTex]

[BibTex]


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Robot Learning

Sigaud, O., Peters, J.

In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 (inbook)

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Web [BibTex]

Web [BibTex]


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Reinforcement Learning in Robotics: A Survey

Kober, J., Peters, J.

In Reinforcement Learning, 12, pages: 579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012 (inbook)

Abstract
As most action generation problems of autonomous robots can be phrased in terms of sequential decision problems, robotics offers a tremendously important and interesting application platform for reinforcement learning. Similarly, the real-world challenges of this domain pose a major real-world check for reinforcement learning. Hence, the interplay between both disciplines can be seen as promising as the one between physics and mathematics. Nevertheless, only a fraction of the scientists working on reinforcement learning are sufficiently tied to robotics to oversee most problems encountered in this context. Thus, we will bring the most important challenges faced by robot reinforcement learning to their attention. To achieve this goal, we will attempt to survey most work that has successfully applied reinforcement learning to behavior generation for real robots. We discuss how the presented successful approaches have been made tractable despite the complexity of the domain and will study how representations or the inclusion of prior knowledge can make a significant difference. As a result, a particular focus of our chapter lies on the choice between model-based and model-free as well as between value function-based and policy search methods. As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain.

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Web DOI [BibTex]

Web DOI [BibTex]


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Machine Learning and Interpretation in Neuroimaging - Revised Selected and Invited Contributions

Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B.

pages: 266, Springer, Heidelberg, Germany, International Workshop, MLINI, Held at NIPS, 2012, Lecture Notes in Computer Science, Vol. 7263 (proceedings)

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DOI [BibTex]

DOI [BibTex]


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Higher-Order Tensors in Diffusion MRI

Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L.

In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, (Editors: Westin, C. F., Vilanova, A. and Burgeth, B.), Springer, 2012 (inbook) Accepted

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[BibTex]

[BibTex]


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MICCAI, Workshop on Computational Diffusion MRI, 2012 (electronic publication)

Panagiotaki, E., O’Donnell, L., Schultz, T., Zhang, G.

15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Workshop on Computational Diffusion MRI , 2012 (proceedings)

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PDF [BibTex]

PDF [BibTex]


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Automated Tip-Based 2-D Mechanical Assembly of Micro/Nanoparticles

Onal, C. D., Ozcan, O., Sitti, M.

In Feedback Control of MEMS to Atoms, pages: 69-108, Springer US, 2012 (incollection)

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[BibTex]

[BibTex]

2004


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Advanced Lectures on Machine Learning

Bousquet, O., von Luxburg, U., Rätsch, G.

ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

Abstract
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in T{\"u}bingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

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Web [BibTex]

2004


Web [BibTex]


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Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175

Rasmussen, C., Bülthoff, H., Giese, M., Schölkopf, B.

Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

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Web DOI [BibTex]

Web DOI [BibTex]


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Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference

Thrun, S., Saul, L., Schölkopf, B.

Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003), pages: 1621, MIT Press, Cambridge, MA, USA, 17th Annual Conference on Neural Information Processing Systems (NIPS), June 2004 (proceedings)

Abstract
The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.

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Web [BibTex]

Web [BibTex]


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Distributed Command Execution

Stark, S., Berlin, M.

In BSD Hacks: 100 industrial-strength tips & tools, pages: 152-152, (Editors: Lavigne, Dru), O’Reilly, Beijing, May 2004 (inbook)

Abstract
Often you want to execute a command not only on one computer, but on several at once. For example, you might want to report the current statistics on a group of managed servers or update all of your web servers at once.

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[BibTex]

[BibTex]


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Gaussian Processes in Machine Learning

Rasmussen, CE.

In 3176, pages: 63-71, Lecture Notes in Computer Science, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, 2004, Copyright by Springer (inbook)

Abstract
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.

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PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Protein Classification via Kernel Matrix Completion

Kin, T., Kato, T., Tsuda, K.

In pages: 261-274, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

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PDF [BibTex]

PDF [BibTex]


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Statistische Lerntheorie und Empirische Inferenz

Schölkopf, B.

Jahrbuch der Max-Planck-Gesellschaft, 2004, pages: 377-382, 2004 (misc)

Abstract
Statistical learning theory studies the process of inferring regularities from empirical data. The fundamental problem is what is called generalization: how it is possible to infer a law which will be valid for an infinite number of future observations, given only a finite amount of data? This problem hinges upon fundamental issues of statistics and science in general, such as the problems of complexity of explanations, a priori knowledge, and representation of data.

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PDF Web [BibTex]

PDF Web [BibTex]


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Introduction to Statistical Learning Theory

Bousquet, O., Boucheron, S., Lugosi, G.

In Lecture Notes in Artificial Intelligence 3176, pages: 169-207, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

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PDF [BibTex]

PDF [BibTex]


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A Primer on Kernel Methods

Vert, J., Tsuda, K., Schölkopf, B.

In Kernel Methods in Computational Biology, pages: 35-70, (Editors: B Schölkopf and K Tsuda and JP Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

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PDF [BibTex]

PDF [BibTex]


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Concentration Inequalities

Boucheron, S., Lugosi, G., Bousquet, O.

In Lecture Notes in Artificial Intelligence 3176, pages: 208-240, (Editors: Bousquet, O., U. von Luxburg and G. Rätsch), Springer, Heidelberg, Germany, 2004 (inbook)

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PDF [BibTex]

PDF [BibTex]


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Kernels for graphs

Kashima, H., Tsuda, K., Inokuchi, A.

In pages: 155-170, (Editors: Schoelkopf, B., K. Tsuda and J.P. Vert), MIT Press, Cambridge, MA; USA, 2004 (inbook)

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PDF [BibTex]

PDF [BibTex]


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A primer on molecular biology

Zien, A.

In pages: 3-34, (Editors: Schoelkopf, B., K. Tsuda and J. P. Vert), MIT Press, Cambridge, MA, USA, 2004 (inbook)

Abstract
Modern molecular biology provides a rich source of challenging machine learning problems. This tutorial chapter aims to provide the necessary biological background knowledge required to communicate with biologists and to understand and properly formalize a number of most interesting problems in this application domain. The largest part of the chapter (its first section) is devoted to the cell as the basic unit of life. Four aspects of cells are reviewed in sequence: (1) the molecules that cells make use of (above all, proteins, RNA, and DNA); (2) the spatial organization of cells (``compartmentalization''); (3) the way cells produce proteins (``protein expression''); and (4) cellular communication and evolution (of cells and organisms). In the second section, an overview is provided of the most frequent measurement technologies, data types, and data sources. Finally, important open problems in the analysis of these data (bioinformatics challenges) are briefly outlined.

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PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]