Publications

Empirical Inference Book Chapter Natural Language Processing Jin, Z., Mihalcea, R., Schölkopf, B. In Elgar Encyclopedia of Political Communication, (Editors: Nai, A. and Grömping, M. and Wirz, D.), Edward Elgar Publishing, 2025 (Published) PDF URL BibTeX

Empirical Inference Book Chapter Natural Language Processing for Policymaking Jin, Z., Mihalcea, R. In Handbook of Computational Social Science for Policy, 141-162, 7, (Editors: Bertoni, E. and Fontana, M. and Gabrielli, L. and Signorelli, S. and Vespe, M.), Springer International Publishing, 2023 (Published) DOI BibTeX

Empirical Inference Book Chapter CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations Salewski, L., Koepke, A. S., Lensch, H. P. A., Akata, Z. In xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, 69-88, (Editors: Holzinger, Andreas and Goebel, Randy and Fong, Ruth and Moon, Taesup and Müller, Klaus-Robert and Samek, Wojciech), Springer International Publishing, 2022 (Published) DOI BibTeX

Empirical Inference Book Chapter Causal Models for Dynamical Systems Peters, J., Bauer, S., Pfister, N. In Probabilistic and Causal Inference: The Works of Judea Pearl, 671-690, 1, Association for Computing Machinery, 2022 (Published) arXiv DOI BibTeX

Empirical Inference Book Chapter Causality for Machine Learning Schölkopf, B. In Probabilistic and Causal Inference: The Works of Judea Pearl, 765-804, 1, Association for Computing Machinery, New York, NY, USA, 2022 (Published) arXiv DOI BibTeX

Empirical Inference Probabilistic Learning Group Book Chapter Towards Causal Algorithmic Recourse Karimi, A. H., von Kügelgen, J., Schölkopf, B., Valera, I. In xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, 139-166, (Editors: Holzinger, Andreas and Goebel, Randy and Fong, Ruth and Moon, Taesup and Müller, Klaus-Robert and Samek, Wojciech), Springer International Publishing, 2022 (Published) DOI BibTeX

Empirical Inference Book Chapter Maschinelles Lernen: Entwicklung ohne Grenzen? Schölkopf, B. In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (Published) BibTeX

Empirical Inference Book Chapter Methods in Psychophysics Wichmann, F. A., Jäkel, F. In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (Published) BibTeX

Empirical Inference Book Chapter Transfer Learning for BCIs Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M. In Brain–Computer Interfaces Handbook, 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (Published) BibTeX

Empirical Inference Book Chapter Policy Gradient Methods Peters, J., Bagnell, J. In Encyclopedia of Machine Learning and Data Mining, 982-985, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (Published) URL BibTeX

Empirical Inference Autonomous Motion Book Chapter Robot Learning Peters, J., Lee, D., Kober, J., Nguyen-Tuong, D., Bagnell, J., Schaal, S. In Springer Handbook of Robotics, 357-394, 15, 2nd, (Editors: Siciliano, Bruno and Khatib, Oussama), Springer International Publishing, 2017 (Published) BibTeX

Empirical Inference Book Chapter Robot Learning Peters, J., Tedrake, R., Roy, N., Morimoto, J. In Encyclopedia of Machine Learning and Data Mining, 1106-1109, 2nd, (Editors: Sammut, Claude and Webb, Geoffrey I.), Springer US, 2017 (Published) DOI BibTeX

Empirical Inference Book Chapter Statistical Asymmetries Between Cause and Effect Janzing, D. In Time in Physics, 129-139, Tutorials, Schools, and Workshops in the Mathematical Sciences, (Editors: Renner, Renato and Stupar, Sandra), Springer International Publishing, Cham, 2017 (Published) DOI URL BibTeX

Empirical Inference Book Chapter 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), 151-167, Mathematics and Visualization, (Editors: Burch, M., Chuang, L., Fisher, B., Schmidt, A., and Weiskopf, D.), Springer, 2017 (Published) DOI BibTeX

Empirical Inference Book Chapter A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis Hohmann, M., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M. In Brain-Computer Interfaces: Lab Experiments to Real-World Applications, 228(Supplement C):221-239, 8, Progress in Brain Research, (Editors: Damien Coyle), Elsevier, 2016 (Published) DOI BibTeX

Empirical Inference Book Chapter Nonlinear functional causal models for distinguishing cause from effect Zhang, K., Hyvärinen, A. In Statistics and Causality: Methods for Applied Empirical Research, 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (Published) BibTeX

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

Empirical Inference Book Chapter Justifying Information-Geometric Causal Inference Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B. In Measures of Complexity: Festschrift for Alexey Chervonenkis, 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (Published) DOI BibTeX

Empirical Inference Book Chapter Fuzzy Fibers: Uncertainty in dMRI Tractography Schultz, T., Vilanova, A., Brecheisen, R., Kindlmann, G. In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, 79-92, 8, Mathematics + Visualization, (Editors: Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E. and Hagen, H.), Springer, 2014 (Published) BibTeX

Empirical Inference Book Chapter Higher-Order Tensors in Diffusion Imaging 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, 129-161, Mathematics + Visualization, (Editors: Westin, C.-F., Vilanova, A. and Burgeth, B.), Springer, 2014 (Published) BibTeX

Empirical Inference Book Chapter Nonconvex Proximal Splitting with Computational Errors Sra, S. In Regularization, Optimization, Kernels, and Support Vector Machines, 83-102, 4, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), CRC Press, 2014 (Published) BibTeX

Empirical Inference Book Chapter Single-Source Domain Adaptation with Target and Conditional Shift Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C. In Regularization, Optimization, Kernels, and Support Vector Machines, 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 BibTeX

Empirical Inference Book Chapter A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs) Grosse-Wentrup, M., Schölkopf, B. In Brain-Computer Interface Research, 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 PDF DOI BibTeX

Empirical Inference Book Chapter On the Relations and Differences between Popper Dimension, Exclusion Dimension and VC-Dimension Seldin, Y., Schölkopf, B. In Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 53-57, 6, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (Published) BibTeX

Empirical Inference Book Chapter Semi-supervised learning in causal and anticausal settings Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J. In Empirical Inference, 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 DOI BibTeX

Empirical Inference Book Chapter Tractable large-scale optimization in machine learning Sra, S. In Tractability: Practical Approaches to Hard Problems, 202-230, 7, (Editors: Bordeaux, L., Hamadi , Y., Kohli, P. and Mateescu, R. ), Cambridge University Press , 2013 (Published) BibTeX

Empirical Inference Book Chapter 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 (In press) PDF BibTeX

Empirical Inference Book Chapter 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 (Accepted) BibTeX

Empirical Inference Book Chapter Inferential structure determination from NMR data Habeck, M. In Bayesian methods in structural bioinformatics, 287-312, (Editors: Hamelryck, T., Mardia, K. V. and Ferkinghoff-Borg, J.), Springer, New York, 2012 BibTeX

Empirical Inference Book Chapter Reinforcement Learning in Robotics: A Survey Kober, J., Peters, J. In Reinforcement Learning, 12:579-610, (Editors: Wiering, M. and Otterlo, M.), Springer, Berlin, Germany, 2012

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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Empirical Inference Book Chapter Robot Learning Sigaud, O., Peters, J. In Encyclopedia of the sciences of learning, (Editors: Seel, N.M.), Springer, Berlin, Germany, 2012 Web BibTeX

Empirical Inference Book Chapter Projected Newton-type methods in machine learning Schmidt, M., Kim, D., Sra, S. In Optimization for Machine Learning, 305-330, (Editors: Sra, S., Nowozin, S. and Wright, S. J.), MIT Press, Cambridge, MA, USA, December 2011

Abstract ›

We consider projected Newton-type methods for solving large-scale optimization problems arising in machine learning and related fields. We first introduce an algorithmic framework for projected Newton-type methods by reviewing a canonical projected (quasi-)Newton method. This method, while conceptually pleasing, has a high computation cost per iteration. Thus, we discuss two variants that are more scalable, namely, two-metric projection and inexact projection methods. Finally, we show how to apply the Newton-type framework to handle non-smooth objectives. Examples are provided throughout the chapter to illustrate machine learning applications of our framework.

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Empirical Inference Book Chapter Statistical Learning Theory: Models, Concepts, and Results von Luxburg, U., Schölkopf, B. In Handbook of the History of Logic, Vol. 10: Inductive Logic, 10:651-706, (Editors: Gabbay, D. M., Hartmann, S. and Woods, J. H.), Elsevier North Holland, Amsterdam, Netherlands, May 2011

Abstract ›

Statistical learning theory provides the theoretical basis for many of today's machine learning algorithms and is arguably one of the most beautifully developed branches of artificial intelligence in general. It originated in Russia in the 1960s and gained wide popularity in the 1990s following the development of the so-called Support Vector Machine (SVM), which has become a standard tool for pattern recognition in a variety of domains ranging from computer vision to computational biology. Providing the basis of new learning algorithms, however, was not the only motivation for developing statistical learning theory. It was just as much a philosophical one, attempting to answer the question of what it is that allows us to draw valid conclusions from empirical data. In this article we attempt to give a gentle, non-technical overview over the key ideas and insights of statistical learning theory. We do not assume that the reader has a deep background in mathematics, statistics, or computer science. Given the nature of the subject matter, however, some familiarity with mathematical concepts and notations and some intuitive understanding of basic probability is required. There exist many excellent references to more technical surveys of the mathematics of statistical learning theory: the monographs by one of the founders of statistical learning theory ([Vapnik, 1995], [Vapnik, 1998]), a brief overview over statistical learning theory in Section 5 of [Sch{\"o}lkopf and Smola, 2002], more technical overview papers such as [Bousquet et al., 2003], [Mendelson, 2003], [Boucheron et al., 2005], [Herbrich and Williamson, 2002], and the monograph [Devroye et al., 1996].

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Empirical Inference Book Chapter Robot Learning Peters, J., Tedrake, R., Roy, N., Morimoto, J. In Encyclopedia of Machine Learning, 865-869, Encyclopedia of machine learning, (Editors: Sammut, C. and Webb, G. I.), Springer, New York, NY, USA, January 2011 PDF Web DOI BibTeX

Empirical Inference Book Chapter Cue Combination: Beyond Optimality Rosas, P., Wichmann, F. In Sensory Cue Integration, 144-152, (Editors: Trommershäuser, J., Körding, K. and Landy, M. S.), Oxford University Press, 2011 BibTeX

Empirical Inference Book Chapter Kernel Methods in Bioinformatics Borgwardt, K. In Handbook of Statistical Bioinformatics, 317-334, Springer Handbooks of Computational Statistics ; 3, (Editors: Lu, H.H.-S., Schölkopf, B. and Zhao, H.), Springer, Berlin, Germany, 2011

Abstract ›

Kernel methods have now witnessed more than a decade of increasing popularity in the bioinformatics community. In this article, we will compactly review this development, examining the areas in which kernel methods have contributed to computational biology and describing the reasons for their success.

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Empirical Inference Book Chapter What You Expect Is What You Get? Potential Use of Contingent Negative Variation for Passive BCI Systems in Gaze-Based HCI Ihme, K., Zander, T. In Affective Computing and Intelligent Interaction, 6975:447-456, Lecture Notes in Computer Science, (Editors: D’Mello, S., Graesser, A., Schuller, B. and Martin, J.-C.), Springer, Berlin, Germany, 2011

Abstract ›

When using eye movements for cursor control in human-computer interaction (HCI), it may be difficult to find an appropriate substitute for the click operation. Most approaches make use of dwell times. However, in this context the so-called Midas-Touch-Problem occurs which means that the system wrongly interprets fixations due to long processing times or spontaneous dwellings of the user as command. Lately it has been shown that brain-computer interface (BCI) input bears good prospects to overcome this problem using imagined hand movements to elicit a selection. The current approach tries to develop this idea further by exploring potential signals for the use in a passive BCI, which would have the advantage that the brain signals used as input are generated automatically without conscious effort of the user. To explore event-related potentials (ERPs) giving information about the user’s intention to select an object, 32-channel electroencephalography (EEG) was recorded from ten participants interacting with a dwell-time-based system. Comparing ERP signals during the dwell time with those occurring during fixations on a neutral cross hair, a sustained negative slow cortical potential at central electrode sites was revealed. This negativity might be a contingent negative variation (CNV) reflecting the participants’ anticipation of the upcoming selection. Offline classification suggests that the CNV is detectable in single trial (mean accuracy 74.9 %). In future, research on the CNV should be accomplished to ensure its stable occurence in human-computer interaction and render possible its use as a potential substitue for the click operation.

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Empirical Inference Book Chapter Markerless tracking of Dynamic 3D Scans of Faces Walder, C., Breidt, M., Bülthoff, H., Schölkopf, B., Curio, C. In Dynamic Faces: Insights from Experiments and Computation, 255-276, (Editors: Curio, C., Bülthoff, H. H. and Giese, M. A.), MIT Press, Cambridge, MA, USA, December 2010 Web BibTeX

Empirical Inference Book Chapter Policy Gradient Methods Peters, J., Bagnell, J. In Encyclopedia of Machine Learning, 774-776, (Editors: Sammut, C. and Webb, G. I.), Springer, Berlin, Germany, December 2010 PDF Web DOI BibTeX

Empirical Inference Book Chapter From Motor Learning to Interaction Learning in Robots Sigaud, O., Peters, J. In From Motor Learning to Interaction Learning in Robots, 1-12, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010

Abstract ›

The number of advanced robot systems has been increasing in recent years yielding a large variety of versatile designs with many degrees of freedom. These robots have the potential of being applicable in uncertain tasks outside wellstructured industrial settings. However, the complexity of both systems and tasks is often beyond the reach of classical robot programming methods. As a result, a more autonomous solution for robot task acquisition is needed where robots adaptively adjust their behaviour to the encountered situations and required tasks. Learning approaches pose one of the most appealing ways to achieve this goal. However, while learning approaches are of high importance for robotics, we cannot simply use off-the-shelf methods from the machine learning community as these usually do not scale into the domains of robotics due to excessive computational cost as well as a lack of scalability. Instead, domain appropriate approaches are needed. In this book, we focus on several core domains of robot learning. For accurate task execution, we need motor learning capabilities. For fast learning of the motor tasks, imitation learning offers the most promising approach. Self improvement requires reinforcement learning approaches that scale into the domain of complex robots. Finally, for efficient interaction of humans with robot systems, we will need a form of interaction learning. This chapter provides a general introduction to these issues and briefly presents the contributions of the subsequent chapters to the corresponding research topics.

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Empirical Inference Book Chapter Imitation and Reinforcement Learning for Motor Primitives with Perceptual Coupling Kober, J., Mohler, B., Peters, J. In From Motor Learning to Interaction Learning in Robots, 209-225, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010

Abstract ›

Traditional motor primitive approaches deal largely with open-loop policies which can only deal with small perturbations. In this paper, we present a new type of motor primitive policies which serve as closed-loop policies together with an appropriate learning algorithm. Our new motor primitives are an augmented version version of the dynamical system-based motor primitives [Ijspeert et al(2002)Ijspeert, Nakanishi, and Schaal] that incorporates perceptual coupling to external variables. We show that these motor primitives can perform complex tasks such as Ball-in-a-Cup or Kendama task even with large variances in the initial conditions where a skilled human player would be challenged. We initialize the open-loop policies by imitation learning and the perceptual coupling with a handcrafted solution. We first improve the open-loop policies and subsequently the perceptual coupling using a novel reinforcement learning method which is particularly well-suited for dynamical system-based motor primitives.

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Empirical Inference Book Chapter Learning Continuous Grasp Affordances by Sensorimotor Exploration Detry, R., Baseski, E., Popovic, M., Touati, Y., Krüger, N., Kroemer, O., Peters, J., Piater, J. In From Motor Learning to Interaction Learning in Robots, 451-465, Studies in Computational Intelligence ; 264, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010

Abstract ›

We develop means of learning and representing object grasp affordances probabilistically. By grasp affordance, we refer to an entity that is able to assess whether a given relative object-gripper configuration will yield a stable grasp. These affordances are represented with grasp densities, continuous probability density functions defined on the space of 3D positions and orientations. Grasp densities are registered with a visual model of the object they characterize. They are exploited by aligning them to a target object using visual pose estimation. Grasp densities are refined through experience: A robot “plays” with an object by executing grasps drawn randomly for the object’s grasp density. The robot then uses the outcomes of these grasps to build a richer density through an importance sampling mechanism. Initial grasp densities, called hypothesis densities, are bootstrapped from grasps collected using a motion capture system, or from grasps generated from the visual model of the object. Refined densities, called empirical densities, represent affordances that have been confirmed through physical experience. The applicability of our method is demonstrated by producing empirical densities for two object with a real robot and its 3-finger hand. Hypothesis densities are created from visual cues and human demonstration.

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Empirical Inference Book Chapter Real-Time Local GP Model Learning Nguyen-Tuong, D., Seeger, M., Peters, J. In From Motor Learning to Interaction Learning in Robots, 264:193-207, Studies in Computational Intelligence, (Editors: Sigaud, O. and Peters, J.), Springer, Berlin, Germany, January 2010

Abstract ›

For many applications in robotics, accurate dynamics models are essential. However, in some applications, e.g., in model-based tracking control, precise dynamics models cannot be obtained analytically for sufficiently complex robot systems. In such cases, machine learning offers a promising alternative for approximating the robot dynamics using measured data. However, standard regression methods such as Gaussian process regression (GPR) suffer from high computational complexity which prevents their usage for large numbers of samples or online learning to date. In this paper, we propose an approximation to the standard GPR using local Gaussian processes models inspired by [Vijayakumar et al(2005)Vijayakumar, DSouza, and Schaal, Snelson and Ghahramani(2007)]. Due to reduced computational cost, local Gaussian processes (LGP) can be applied for larger sample-sizes and online learning. Comparisons with other nonparametric regressions, e.g., standard GPR, support vector regression (SVR) and locally weighted proje ction regression (LWPR), show that LGP has high approximation accuracy while being sufficiently fast for real-time online learning.

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Empirical Inference Book Chapter Approaches Based on Support Vector Machine to Classification of Remote Sensing Data Bruzzone, L., Persello, C. In Handbook of Pattern Recognition and Computer Vision, 329-352, (Editors: Chen, C.H.), ICP, London, UK, 2010

Abstract ›

This chapter presents an extensive and critical review on the use of kernel methods and in particular of support vector machines (SVMs) in the classification of remote-sensing (RS) data. The chapter recalls the mathematical formulation and the main theoretical concepts related to SVMs, and discusses the motivations at the basis of the use of SVMs in remote sensing. A review on the main applications of SVMs in classification of remote sensing is given, presenting a literature survey on the use of SVMs for the analysis of different kinds of RS images. In addition, the most recent methodological developments related to SVM-based classification techniques in RS are illustrated by focusing on semisupervised, domain adaptation, and context sensitive approaches. Finally, the most promising research directions on SVM in RS are identified and discussed.

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Empirical Inference Book Chapter Machine Learning Methods for Automatic Image Colorization Charpiat, G., Bezrukov, I., Hofmann, M., Altun, Y., Schölkopf, B. In Computational Photography: Methods and Applications, 395-418, Digital Imaging and Computer Vision, (Editors: Lukac, R.), CRC Press, Boca Raton, FL, USA, 2010

Abstract ›

We aim to color greyscale images automatically, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic colorization is nontrivial since there is usually no one-to-one correspondence between color and local texture. The contribution of our framework is that we deal directly with multimodality and estimate, for each pixel of the image to be colored, the probability distribution of all possible colors, instead of choosing the most probable color at the local level. We also predict the expected variation of color at each pixel, thus defining a non-uniform spatial coherency criterion. We then use graph cuts to maximize the probability of the whole colored image at the global level. We work in the L-a-b color space in order to approximate the human perception of distances between colors, and we use machine learning tools to extract as much information as possible from a dataset of colored examples. The resulting algorithm is fast, designed to be more robust to texture noise, and is above all able to deal with ambiguity, in contrary to previous approaches.

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Empirical Inference Book Chapter Text Clustering with Mixture of von Mises-Fisher Distributions Sra, S., Banerjee, A., Ghosh, J., Dhillon, I. In Text mining: classification, clustering, and applications, 121-161, Chapman & Hall/CRC data mining and knowledge discovery series, (Editors: Srivastava, A. N. and Sahami, M.), CRC Press, Boca Raton, FL, USA, June 2009 Web DOI BibTeX

Empirical Inference Book Chapter Data Mining for Biologists Tsuda, K. In Biological Data Mining in Protein Interaction Networks, 14-27, (Editors: Li, X. and Ng, S.-K.), Medical Information Science Reference, Hershey, PA, USA, May 2009

Abstract ›

In this tutorial chapter, we review basics about frequent pattern mining algorithms, including itemset mining, association rule mining and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures and chemical compounds. As they have been primarily used for business data, biological applications are not so common yet, but their potential impact would be large. Recent advances in computers including multicore machines and ever increasing memory capacity support the application of such methods to larger datasets. We explain technical aspects of the algorithms, but do not go into details. Current biological applications are summarized and possible future directions are given.

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Empirical Inference Book Chapter Large Margin Methods for Part of Speech Tagging Altun, Y. In Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods, 141-160, (Editors: Keshet, J. and Bengio, S.), Wiley, Hoboken, NJ, USA, January 2009 Web BibTeX

Empirical Inference Book Chapter Covariate shift and local learning by distribution matching Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Schölkopf, B. In Dataset Shift in Machine Learning, 131-160, (Editors: Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, N. D.), MIT Press, Cambridge, MA, USA, 2009

Abstract ›

Given sets of observations of training and test data, we consider the problem of re-weighting the training data such that its distribution more closely matches that of the test data. We achieve this goal by matching covariate distributions between training and test sets in a high dimensional feature space (specifically, a reproducing kernel Hilbert space). This approach does not require distribution estimation. Instead, the sample weights are obtained by a simple quadratic programming procedure. We provide a uniform convergence bound on the distance between the reweighted training feature mean and the test feature mean, a transductive bound on the expected loss of an algorithm trained on the reweighted data, and a connection to single class SVMs. While our method is designed to deal with the case of simple covariate shift (in the sense of Chapter ??), we have also found benefits for sample selection bias on the labels. Our correction procedure yields its greatest and most consistent advantages when the learning algorithm returns a classifier/regressor that is simpler" than the data might suggest.

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Empirical Inference Book Chapter New Frontiers in Characterizing Structure and Dynamics by NMR Nilges, M., Markwick, P., Malliavin, T., Rieping, W., Habeck, M. In Computational Structural Biology: Methods and Applications, 655-680, (Editors: Schwede, T. , M. C. Peitsch), World Scientific, New Jersey, NJ, USA, May 2008

Abstract ›

Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as the method of choice for studying both the structure and the dynamics of biological macromolecule in solution. Despite the maturity of the NMR method for structure determination, its application faces a number of challenges. The method is limited to systems of relatively small molecular mass, data collection times are long, data analysis remains a lengthy procedure, and it is difficult to evaluate the quality of the final structures. The last years have seen significant advances in experimental techniques to overcome or reduce some limitations. The function of bio-macromolecules is determined by both their 3D structure and conformational dynamics. These molecules are inherently flexible systems displaying a broad range of dynamics on timescales from picoseconds to seconds. NMR is unique in its ability to obtain dynamic information on an atomic scale. The experimental information on structure and dynamics is intricately mixed. It is however difficult to unite both structural and dynamical information into one consistent model, and protocols for the determination of structure and dynamics are performed independently. This chapter deals with the challenges posed by the interpretation of NMR data on structure and dynamics. We will first relate the standard structure calculation methods to Bayesian probability theory. We will then briefly describe the advantages of a fully Bayesian treatment of structure calculation. Then, we will illustrate the advantages of using Bayesian reasoning at least partly in standard structure calculations. The final part will be devoted to interpretation of experimental data on dynamics.

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