Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

45 results (View BibTeX file of all listed publications)

2008

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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)

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Logistic Regression for Graph Classification

Shervashidze, N., Tsuda, K.

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)

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New Projected Quasi-Newton Methods with Applications

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)

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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)

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Nonparametric Indepedence Tests: Space Partitioning and Kernel Approaches

Gretton, A., Györfi, L.

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

PDF Web [BibTex]

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

PDF Web [BibTex]

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Data-driven goodness-of-fit tests

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)

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Reinforcement Learning for Motor Primitives

Biologische Kybernetik, University of Stuttgart, Stuttgart, Germany, August 2008 (diplomathesis)

Kober, J. Reinforcement Learning for Motor Primitives Biologische Kybernetik, University of Stuttgart, Stuttgart, Germany, August 2008 (diplomathesis)

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Asymmetries of Time Series under Inverting their Direction

Biologische Kybernetik, University of Heidelberg, August 2008 (diplomathesis)

Peters, J. Asymmetries of Time Series under Inverting their Direction Biologische Kybernetik, University of Heidelberg, August 2008 (diplomathesis)

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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)

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Learning an Interest Operator from Human Eye Movements

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, July 2008 (phdthesis)

Kienzle, W. Learning an Interest Operator from Human Eye Movements Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, July 2008 (phdthesis)

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Discovering Common Sequence Variation in Arabidopsis thaliana

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.

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)

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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)

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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)

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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.

PDF Web [BibTex]

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)

PDF Web [BibTex]

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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)

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Multi-Classification by Categorical Features via Clustering

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.

PDF Web [BibTex]

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

PDF Web [BibTex]

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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)

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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)

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Causal inference from statistical data

Biologische Kybernetik, Technische Hochschule Karlsruhe, Karlsruhe, Germany, April 2008 (phdthesis)

Sun, X. Causal inference from statistical data Biologische Kybernetik, Technische Hochschule Karlsruhe, Karlsruhe, Germany, April 2008 (phdthesis)

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Pairwise Correlations and Multineuronal Firing Patterns in Primary Visual Cortex

Berens, P.

Biologische Kybernetik, Eberhard Karls Universität Tübingen, Tübingen, Germany, April 2008 (diplomathesis)

Berens, P. Pairwise Correlations and Multineuronal Firing Patterns in Primary Visual Cortex Biologische Kybernetik, Eberhard Karls Universität Tübingen, Tübingen, Germany, April 2008 (diplomathesis)

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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)

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Development and Application of a Python Scripting Framework for BCI2000

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, January 2008 (diplomathesis)

Schreiner, T. Development and Application of a Python Scripting Framework for BCI2000 Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, January 2008 (diplomathesis)

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Efficient and Invariant Regularisation with Application to Computer Graphics

Walder, CJ.

Biologische Kybernetik, University of Queensland, Brisbane, Australia, January 2008 (phdthesis)

Abstract

This thesis develops the theory and practise of reproducing kernel methods. Many functional inverse problems which arise in, for example, machine learning and computer graphics, have been treated with practical success using methods based on a reproducing kernel Hilbert space perspective. This perspective is often theoretically convenient, in that many functional analysis problems reduce to linear algebra problems in these spaces. Somewhat more complex is the case of conditionally positive definite kernels, and we provide an introduction to both cases, deriving in a particularly elementary manner some key results for the conditionally positive definite case. A common complaint of the practitioner is the long running time of these kernel based algorithms. We provide novel ways of alleviating these problems by essentially using a non-standard function basis which yields computational advantages. That said, by doing so we must also forego the aforementioned theoretical conveniences, and hence need some additional analysis which we provide in order to make the approach practicable. We demonstrate that the method leads to state of the art performance on the problem of surface reconstruction from points. We also provide some analysis of kernels invariant to transformations such as translation and dilation, and show that this indicates the value of learning algorithms which use conditionally positive definite kernels. Correspondingly, we provide a few approaches for making such algorithms practicable. We do this either by modifying the kernel, or directly solving problems with conditionally positive definite kernels, which had previously only been solved with positive definite kernels. We demonstrate the advantage of this approach, in particular by attaining state of the art classification performance with only one free parameter.

Walder, CJ. Efficient and Invariant Regularisation with Application to Computer Graphics Biologische Kybernetik, University of Queensland, Brisbane, Australia, January 2008 (phdthesis)

2004

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How to learn from very few examples?

October 2004 (talk)

2004

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

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Discrete vs. Continuous: Two Sides of Machine Learning

October 2004 (talk)

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

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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)

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Independent component analysis and beyond

Biologische Kybernetik, Universität Potsdam, Potsdam, October 2004 (phdthesis)

Harmeling, S. Independent component analysis and beyond Biologische Kybernetik, Universität Potsdam, Potsdam, October 2004 (phdthesis)

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Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung

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)

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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)

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Riemannian Geometry on Graphs and its Application to Ranking and Classification

June 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.

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

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Exploration of combining Echo-State Network Learning with Recurrent Neural Network Learning techniques

Biologische Kybernetik, International University Bremen, Bremen, Germany, May 2004 (diplomathesis)

Erhan, D. Exploration of combining Echo-State Network Learning with Recurrent Neural Network Learning techniques Biologische Kybernetik, International University Bremen, Bremen, Germany, May 2004 (diplomathesis)

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Computational Analysis of Gene Expression Data

(4), Biologische Kybernetik, March 2004 (phdthesis)

Zien, A. Computational Analysis of Gene Expression Data (4), Biologische Kybernetik, March 2004 (phdthesis)

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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)

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Introduction to Category Theory

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.

Bousquet, O. Introduction to Category Theory Internal Seminar, January 2004 (talk)

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The p53 Oligomerization Domain: Sequence-Structure Relationships and the Design and Characterization of Altered Oligomeric States

Davison, TS.

University of Toronto, Canada, University of Toronto, Canada, 2004 (phdthesis)

Davison, TS. The p53 Oligomerization Domain: Sequence-Structure Relationships and the Design and Characterization of Altered Oligomeric States University of Toronto, Canada, University of Toronto, Canada, 2004 (phdthesis)

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Statistical Learning with Similarity and Dissimilarity Functions

von Luxburg, U.

pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

PDF PostScript [BibTex]

von Luxburg, U. Statistical Learning with Similarity and Dissimilarity Functions pages: 1-166, Technische Universität Berlin, Germany, Technische Universität Berlin, Germany, 2004 (phdthesis)

PDF PostScript [BibTex]

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Classification and Feature Extraction in Man and Machine

Graf, AAB.

Biologische Kybernetik, University of Tübingen, Germany, 2004, online publication (phdthesis)

Graf, AAB. Classification and Feature Extraction in Man and Machine Biologische Kybernetik, University of Tübingen, Germany, 2004, online publication (phdthesis)

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Advanced Statistical Learning Theory

Machine Learning Summer School, 2004 (talk)

Bousquet, O. Advanced Statistical Learning Theory Machine Learning Summer School, 2004 (talk)

2002

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Nonlinear Multivariate Analysis with Geodesic Kernels

Biologische Kybernetik, Technische Universität Berlin, February 2002 (diplomathesis)

2002

Kuss, M. Nonlinear Multivariate Analysis with Geodesic Kernels Biologische Kybernetik, Technische Universität Berlin, February 2002 (diplomathesis)

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Transductive Learning: Motivation, Models, Algorithms

January 2002 (talk)

Bousquet, O. Transductive Learning: Motivation, Models, Algorithms January 2002 (talk)

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Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms

Biologische Kybernetik, Ecole Polytechnique, 2002 (phdthesis) Accepted

Abstract

New classification algorithms based on the notion of 'margin' (e.g. Support Vector Machines, Boosting) have recently been developed. The goal of this thesis is to better understand how they work, via a study of their theoretical performance. In order to do this, a general framework for real-valued classification is proposed. In this framework, it appears that the natural tools to use are Concentration Inequalities and Empirical Processes Theory. Thanks to an adaptation of these tools, a new measure of the size of a class of functions is introduced, which can be computed from the data. This allows, on the one hand, to better understand the role of eigenvalues of the kernel matrix in Support Vector Machines, and on the other hand, to obtain empirical model selection criteria.

PostScript [BibTex]

Bousquet, O. Concentration Inequalities and Empirical Processes Theory Applied to the Analysis of Learning Algorithms Biologische Kybernetik, Ecole Polytechnique, 2002 (phdthesis) Accepted

PostScript [BibTex]

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Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge

Biologische Kybernetik, 2002 (phdthesis)

Abstract

This thesis presents a theoretical and practical study of Support Vector Machines (SVM) and related learning algorithms. In a first part, we introduce a new induction principle from which SVMs can be derived, but some new algorithms are also presented in this framework. In a second part, after studying how to estimate the generalization error of an SVM, we suggest to choose the kernel parameters of an SVM by minimizing this estimate. Several applications such as feature selection are presented. Finally the third part deals with the incoporation of prior knowledge in a learning algorithm and more specifically, we studied the case of known invariant transormations and the use of unlabeled data.

Chapelle, O. Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge Biologische Kybernetik, 2002 (phdthesis)

2000

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Three-dimensional reconstruction of planar scenes

Biologische Kybernetik, INP Grenoble, Warsaw University of Technology, September 2000 (diplomathesis)

Abstract

For a planar scene, we propose an algorithm to estimate its 3D structure. Homographies between corresponding planes are employed in order to recover camera motion parameters - between camera positions from which images of the scene were taken. Cases of one- and multiple- corresponding planes present on the scene are distinguished. Solutions are proposed for both cases.

2000

Urbanek, M. Three-dimensional reconstruction of planar scenes Biologische Kybernetik, INP Grenoble, Warsaw University of Technology, September 2000 (diplomathesis)

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Intelligence as a Complex System

Biologische Kybernetik, 2000 (phdthesis)

Zhou, D. Intelligence as a Complex System Biologische Kybernetik, 2000 (phdthesis)

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Neural Networks in Robot Control

Biologische Kybernetik, Fernuniversität Hagen, Hagen, Germany, 2000 (diplomathesis)

Peters, J. Neural Networks in Robot Control Biologische Kybernetik, Fernuniversität Hagen, Hagen, Germany, 2000 (diplomathesis)

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems

Latest News

Bernhard Schölkopf meets French President Emmanuel Macron 30 October 2019
Verleihung des Körber-Preises 2019 04 September 2019
NeurIPS2019 Disentanglement Challenge kicks off - Bringing Disentanglement to the Real World 03 July 2019
Körber Prize 2019 goes to Bernhard Schölkopf 26 June 2019

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