Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

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 (talk)

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

Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

Besserve, M. Independence of cause and mechanism in brain networks DALI workshop on Networks: Processes and Causality, April 2015 (talk)

Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D. Information-Theoretic Implications of Classical and Quantum Causal Structures 18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J. Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI World Molecular Imaging Conference, 2015 (talk)

Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J. Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model World Molecular Imaging Conference, 2015 (talk)

The search for single exoplanet transits in the Kepler light curves

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

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B. The search for single exoplanet transits in the Kepler light curves IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

2008

BCPy2000

Hill, N., Schreiner, T., Puzicha, C., Farquhar, J.

Workshop "Machine Learning Open-Source Software" at NIPS, December 2008 (talk)

2008

Hill, N., Schreiner, T., Puzicha, C., Farquhar, J. BCPy2000 Workshop "Machine Learning Open-Source Software" at NIPS, December 2008 (talk)

Shervashidze, N., Tsuda, K.

Logistic Regression for Graph Classification

NIPS Workshop on "Structured Input - Structured Output" (NIPS SISO), December 2008 (talk)

Abstract

In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression for graphs, which is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics.

Shervashidze, N., Tsuda, K. Logistic Regression for Graph Classification NIPS Workshop on "Structured Input - Structured Output" (NIPS SISO), December 2008 (talk)

New Projected Quasi-Newton Methods with Applications

Sra, S.

Microsoft Research Tech-talk, December 2008 (talk)

Abstract

Box-constrained convex optimization problems are central to several applications in a variety of fields such as statistics, psychometrics, signal processing, medical imaging, and machine learning. Two fundamental examples are the non-negative least squares (NNLS) problem and the non-negative Kullback-Leibler (NNKL) divergence minimization problem. The non-negativity constraints are usually based on an underlying physical restriction, for e.g., when dealing with applications in astronomy, tomography, statistical estimation, or image restoration, the underlying parameters represent physical quantities such as concentration, weight, intensity, or frequency counts and are therefore only interpretable with non-negative values. Several modern optimization methods can be inefficient for simple problems such as NNLS and NNKL as they are really designed to handle far more general and complex problems. In this work we develop two simple quasi-Newton methods for solving box-constrained (differentiable) convex optimization problems that utilize the well-known BFGS and limited memory BFGS updates. We position our method between projected gradient (Rosen, 1960) and projected Newton (Bertsekas, 1982) methods, and prove its convergence under a simple Armijo step-size rule. We illustrate our method by showing applications to: Image deblurring, Positron Emission Tomography (PET) image reconstruction, and Non-negative Matrix Approximation (NMA). On medium sized data we observe performance competitive to established procedures, while for larger data the results are even better.

Sra, S. New Projected Quasi-Newton Methods with Applications Microsoft Research Tech-talk, December 2008 (talk)

MR-Based PET Attenuation Correction: Initial Results for Whole Body

Hofmann, M., Steinke, F., Aschoff, P., Lichy, M., Brady, M., Schölkopf, B., Pichler, B.

Medical Imaging Conference, October 2008 (talk)

Hofmann, M., Steinke, F., Aschoff, P., Lichy, M., Brady, M., Schölkopf, B., Pichler, B. MR-Based PET Attenuation Correction: Initial Results for Whole Body Medical Imaging Conference, October 2008 (talk)

Nonparametric Indepedence Tests: Space Partitioning and Kernel Approaches

Gretton, A., Györfi, L.

19th International Conference on Algorithmic Learning Theory (ALT08), October 2008 (talk)

Gretton, A., Györfi, L. Nonparametric Indepedence Tests: Space Partitioning and Kernel Approaches 19th International Conference on Algorithmic Learning Theory (ALT08), October 2008 (talk)

Interactive images

Schölkopf, B., Toyama, K., Uyttendaele, M.

United States Patent, No 7444015, October 2008 (patent)

Schölkopf, B., Toyama, K., Uyttendaele, M. Interactive images United States Patent, No 7444015, October 2008 (patent)

Interactive images

Schölkopf, B., Toyama, K., Uyttendaele, M.

United States Patent, No 7444016, October 2008 (patent)

Schölkopf, B., Toyama, K., Uyttendaele, M. Interactive images United States Patent, No 7444016, October 2008 (patent)

Data-driven goodness-of-fit tests

Langovoy, M.

2008 Barcelona Conference on Asymptotic Statistics (BAS), September 2008 (talk)

Langovoy, M. Data-driven goodness-of-fit tests 2008 Barcelona Conference on Asymptotic Statistics (BAS), September 2008 (talk)

Interactive images

Schölkopf, B., Toyama, K., Uyttendaele, M.

United States Patent, No 7421115, September 2008 (patent)

Schölkopf, B., Toyama, K., Uyttendaele, M. Interactive images United States Patent, No 7421115, September 2008 (patent)

mGene: A Novel Discriminative Gene Finder

Schweikert, G., Zeller, G., Zien, A., Behr, J., Sonnenburg, S., Philips, P., Ong, C., Rätsch, G.

Worm Genomics and Systems Biology meeting, July 2008 (talk)

Schweikert, G., Zeller, G., Zien, A., Behr, J., Sonnenburg, S., Philips, P., Ong, C., Rätsch, G. mGene: A Novel Discriminative Gene Finder Worm Genomics and Systems Biology meeting, July 2008 (talk)

Discovering Common Sequence Variation in Arabidopsis thaliana

16th Annual International Conference Intelligent Systems for Molecular Biology (ISMB), July 2008 (talk)

Rätsch, G., Clark, R., Schweikert, G., Toomajian, C., Ossowski, S., Zeller, G., Shinn, P., Warthman, N., Hu, T., Fu, G., Hinds, D., Cheng, H., Frazer, K., Huson, D., Schölkopf, B., Nordborg, M., Ecker, J., Weigel, D., Schneeberger, K., Bohlen, A. Discovering Common Sequence Variation in Arabidopsis thaliana 16th Annual International Conference Intelligent Systems for Molecular Biology (ISMB), July 2008 (talk)

CogRob 2008: The 6th International Cognitive Robotics Workshop

Lespérance, Y., Lakemeyer, G., Peters, J., Pirri, F.

Proceedings of the 6th International Cognitive Robotics Workshop (CogRob 2008), pages: 35, Patras University Press, Patras, Greece, 6th International Cognitive Robotics Workshop (CogRob), July 2008 (proceedings)

Lespérance, Y., Lakemeyer, G., Peters, J., Pirri, F. CogRob 2008: The 6th International Cognitive Robotics Workshop Proceedings of the 6th International Cognitive Robotics Workshop (CogRob 2008), pages: 35, Patras University Press, Patras, Greece, 6th International Cognitive Robotics Workshop (CogRob), July 2008 (proceedings)

Coding Theory in Brain-Computer Interfaces

Martens, SMM.

Soria Summerschool on Computational Mathematics "Algebraic Coding Theory" (S3CM), July 2008 (talk)

Martens, SMM. Coding Theory in Brain-Computer Interfaces Soria Summerschool on Computational Mathematics "Algebraic Coding Theory" (S3CM), July 2008 (talk)

Motor Skill Learning for Cognitive Robotics

6th International Cognitive Robotics Workshop (CogRob), July 2008 (talk)

Abstract

Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this tutorial, we give a general overview on motor skill learning for cognitive robotics using research at ATR, USC, CMU and Max-Planck in order to illustrate the problems in motor skill learning. For doing so, we discuss task-appropriate representations and algorithms for learning robot motor skills. Among the topics are the learning basic movements or motor primitives by imitation and reinforcement learning, learning rhytmic and discrete movements, fast regression methods for learning inverse dynamics and setups for learning task-space policies. Examples on various robots, e.g., SARCOS DB, the SARCOS Master Arm, BDI Little Dog and a Barrett WAM, are shown and include Ball-in-a-Cup, T-Ball, Juggling, Devil-Sticking, Operational Space Control and many others.

Peters, J. Motor Skill Learning for Cognitive Robotics 6th International Cognitive Robotics Workshop (CogRob), July 2008 (talk)

Painless Embeddings of Distributions: the Function Space View (Part 1)

Fukumizu, K., Gretton, A., Smola, A.

25th International Conference on Machine Learning (ICML), July 2008 (talk)

Abstract

This tutorial will give an introduction to the recent understanding and methodology of the kernel method: dealing with higher order statistics by embedding painlessly random variables/probability distributions. In the early days of kernel machines research, the "kernel trick" was considered a useful way of constructing nonlinear algorithms from linear ones. More recently, however, it has become clear that a potentially more far reaching use of kernels is as a linear way of dealing with higher order statistics by embedding distributions in a suitable reproducing kernel Hilbert space (RKHS). Notably, unlike the straightforward expansion of higher order moments or conventional characteristic function approach, the use of kernels or RKHS provides a painless, tractable way of embedding distributions. This line of reasoning leads naturally to the questions: what does it mean to embed a distribution in an RKHS? when is this embedding injective (and thus, when do different distributions have unique mappings)? what implications are there for learning algorithms that make use of these embeddings? This tutorial aims at answering these questions. There are a great variety of applications in machine learning and computer science, which require distribution estimation and/or comparison.

Fukumizu, K., Gretton, A., Smola, A. Painless Embeddings of Distributions: the Function Space View (Part 1) 25th International Conference on Machine Learning (ICML), July 2008 (talk)

Reinforcement Learning for Robotics

8th European Workshop on Reinforcement Learning for Robotics (EWRL), July 2008 (talk)

Peters, J. Reinforcement Learning for Robotics 8th European Workshop on Reinforcement Learning for Robotics (EWRL), July 2008 (talk)

Multi-Classification by Categorical Features via Clustering

Seldin, Y.

25th International Conference on Machine Learning (ICML), June 2008 (talk)

Abstract

We derive a generalization bound for multi-classification schemes based on grid clustering in categorical parameter product spaces. Grid clustering partitions the parameter space in the form of a Cartesian product of partitions for each of the parameters. The derived bound provides a means to evaluate clustering solutions in terms of the generalization power of a built-on classifier. For classification based on a single feature the bound serves to find a globally optimal classification rule. Comparison of the generalization power of individual features can then be used for feature ranking. Our experiments show that in this role the bound is much more precise than mutual information or normalized correlation indices.

Seldin, Y. Multi-Classification by Categorical Features via Clustering 25th International Conference on Machine Learning (ICML), June 2008 (talk)

Thin-Plate Splines Between Riemannian Manifolds

Steinke, F., Hein, M., Schölkopf, B.

Workshop on Geometry and Statistics of Shapes, June 2008 (talk)

Abstract

With the help of differential geometry we describe a framework to define a thin-plate spline like energy for maps between arbitrary Riemannian manifolds. The so-called Eells energy only depends on the intrinsic geometry of the input and output manifold, but not on their respective representation. The energy can then be used for regression between manifolds, we present results for cases where the outputs are rotations, sets of angles, or points on 3D surfaces. In the future we plan to also target regression where the output is an element of "shape space", understood as a Riemannian manifold. One could also further explore the meaning of the Eells energy when applied to diffeomorphisms between shapes, especially with regard to its potential use as a distance measure between shapes that does not depend on the embedding or the parametrisation of the shapes.

Steinke, F., Hein, M., Schölkopf, B. Thin-Plate Splines Between Riemannian Manifolds Workshop on Geometry and Statistics of Shapes, June 2008 (talk)

Pattern detection using reduced set vectors

Blake, A., Romdhani, S., Schölkopf, B., Torr, P. H. S.

United States Patent, No 7391908, June 2008 (patent)

Blake, A., Romdhani, S., Schölkopf, B., Torr, P. H. S. Pattern detection using reduced set vectors United States Patent, No 7391908, June 2008 (patent)

Machine Learning for Robotics: Learning Methods for Robot Motor Skills

pages: 107 , (Editors: J Peters), VDM-Verlag, Saarbrücken, Germany, May 2008 (book)

Abstract

Autonomous robots have been a vision of robotics, artificial intelligence, and cognitive sciences. An important step towards this goal is to create robots that can learn to accomplish amultitude of different tasks triggered by environmental context and higher-level instruction. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s showed that handcrafted approaches do not suffice and that machine learning is needed. However, off the shelf learning techniques often do not scale into real-time or to the high-dimensional domains of manipulator and humanoid robotics. In this book, we investigate the foundations for a general approach to motor skilllearning that employs domain-specific machine learning methods. A theoretically well-founded general approach to representing the required control structures for task representation and executionis presented along with novel learning algorithms that can be applied in this setting. The resulting framework is shown to work well both in simulation and on real robots.

Peters, J. Machine Learning for Robotics: Learning Methods for Robot Motor Skills pages: 107 , (Editors: J Peters), VDM-Verlag, Saarbrücken, Germany, May 2008 (book)

Learning resolved velocity control

2008 IEEE International Conference on Robotics and Automation (ICRA), May 2008 (talk)

Peters, J. Learning resolved velocity control 2008 IEEE International Conference on Robotics and Automation (ICRA), May 2008 (talk)

Bayesian methods for protein structure determination

Habeck, M.

Machine Learning in Structural Bioinformatics, April 2008 (talk)

Habeck, M. Bayesian methods for protein structure determination Machine Learning in Structural Bioinformatics, April 2008 (talk)

Kernels and methods for selecting kernels for use in learning machines

Bartlett, P. L., Elisseeff, A., Schölkopf, B.

United States Patent, No 7353215, April 2008 (patent)

Bartlett, P. L., Elisseeff, A., Schölkopf, B. Kernels and methods for selecting kernels for use in learning machines United States Patent, No 7353215, April 2008 (patent)

Methods for feature selection in a learning machine

Weston, J., Elisseeff, A., Schölkopf, B., Pérez-Cruz, F.

United States Patent, No 7318051, January 2008 (patent)

Weston, J., Elisseeff, A., Schölkopf, B., Pérez-Cruz, F. Methods for feature selection in a learning machine United States Patent, No 7318051, January 2008 (patent)

Haptic Device For Cell Manipulation

Lee, DY., Son, HI., Woo, HJ.

Max-Planck-Gesellschaft, Biologische Kybernetik, 2008 (patent)

Lee, DY., Son, HI., Woo, HJ. Haptic Device For Cell Manipulation Max-Planck-Gesellschaft, Biologische Kybernetik, 2008 (patent)

2004

How to learn from very few examples?

October 2004 (talk)

2004

Zhou, D. How to learn from very few examples? October 2004 (talk)

Discrete vs. Continuous: Two Sides of Machine Learning

October 2004 (talk)

Zhou, D. Discrete vs. Continuous: Two Sides of Machine Learning October 2004 (talk)

Discrete vs. Continuous: Two Sides of Machine Learning

October 2004 (talk)

Abstract

We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data. This talk is mainly based on the followiing contribution: (1) D. Zhou and B. Sch{\"o}lkopf: Transductive Inference with Graphs, MPI Technical report, August, 2004; (2) D. Zhou, B. Sch{\"o}lkopf and T. Hofmann. Semi-supervised Learning on Directed Graphs. NIPS 2004; (3) D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Sch{\"o}lkopf. Learning with Local and Global Consistency. NIPS 2003.

Zhou, D. Discrete vs. Continuous: Two Sides of Machine Learning October 2004 (talk)

Pattern detection methods and systems and face detection methods and systems

Blake, A., Romdhani, S., Schölkopf, B., Torr, P. H. S.

United States Patent, No 6804391, October 2004 (patent)

Blake, A., Romdhani, S., Schölkopf, B., Torr, P. H. S. Pattern detection methods and systems and face detection methods and systems United States Patent, No 6804391, October 2004 (patent)

Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung

Eichhorn, J.

September 2004 (talk)

Abstract

Invited talk at the workshop "Numerical, Statistical and Discrete Methods in Image Processing" at the TU M{\"u}nchen (in GERMAN)

Eichhorn, J. Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung September 2004 (talk)

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

Advanced Lectures on Machine Learning

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.

Bousquet, O., von Luxburg, U., Rätsch, G. Advanced Lectures on Machine Learning ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

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)

Web DOI [BibTex]

Rasmussen, C., Bülthoff, H., Giese, M., Schölkopf, B. Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175 Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

Web DOI [BibTex]

Kernel Methods in Computational Biology

Schölkopf, B., Tsuda, K., Vert, J.

pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)

Abstract

Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology. Following three introductory chaptersan introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biologythe book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods.

Schölkopf, B., Tsuda, K., Vert, J. Kernel Methods in Computational Biology pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)

The benefit of liquid Helium cooling for Cryo-Electron Tomography: A quantitative comparative study

Schweikert, G., Luecken, U., Pfeifer, G., Baumeister, W., Plitzko, J.

The thirteenth European Microscopy Congress, August 2004 (talk)

Schweikert, G., Luecken, U., Pfeifer, G., Baumeister, W., Plitzko, J. The benefit of liquid Helium cooling for Cryo-Electron Tomography: A quantitative comparative study The thirteenth European Microscopy Congress, August 2004 (talk)

Riemannian Geometry on Graphs and its Application to Ranking and Classification

June 2004 (talk)

Abstract

Zhou, D. Riemannian Geometry on Graphs and its Application to Ranking and Classification June 2004 (talk)

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 attendeesphysicists, 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.

Thrun, S., Saul, L., Schölkopf, B. Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference 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)

Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking

January 2004 (talk)

Abstract

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

Zhou, D. Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking January 2004 (talk)

Introduction to Category Theory

Bousquet, O.

Internal Seminar, January 2004 (talk)

Abstract

A brief introduction to the general idea behind category theory with some basic definitions and examples. A perspective on higher dimensional categories is given.