Header logo is


2020


no image
Kernel Conditional Moment Test via Maximum Moment Restriction

Muandet, K., Jitkrittum, W., Kübler, J. M.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), August 2020 (conference) Accepted

ei

[BibTex]

2020


[BibTex]


no image
Bayesian Online Prediction of Change Points

Agudelo-España, D., Gomez-Gonzalez, S., Bauer, S., Schölkopf, B., Peters, J.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), August 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


no image
Algorithmic Recourse: from Counterfactual Explanations to Interventions

Karimi, A., Schölkopf, B., Valera, I.

37th International Conference on Machine Learning (ICML), July 2020 (conference) Submitted

ei plg

[BibTex]

[BibTex]


no image
Model-Agnostic Counterfactual Explanations for Consequential Decisions

Karimi, A., Barthe, G., Balle, B., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), June 2020 (conference) Accepted

ei plg

arXiv [BibTex]

arXiv [BibTex]


no image
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization

F Alimisis, F., Orvieto, A., Becigneul, G., Lucchi, A.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), June 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


no image
Where Does It End? - Reasoning About Hidden Surfaces by Object Intersection Constraints

Strecke, M., Stückler, J.

In Proceedings IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, June 2020, to appear (inproceedings) Accepted

ev

preprint project page [BibTex]

preprint project page [BibTex]


no image
Kernel Conditional Density Operators

Schuster, I., Mollenhauer, M., Klus, S., Muandet, K.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Proceedings of Machine Learning Research, June 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


no image
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control

Zhu, J., Diehl, M., Schölkopf, B.

2nd Annual Conference on Learning for Dynamics and Control (L4DC), June 2020 (conference) Accepted

ei

arXiv [BibTex]

arXiv [BibTex]


no image
Disentangling Factors of Variations Using Few Labels

Locatello, F., Tschannen, M., Bauer, S., Rätsch, G., Schölkopf, B., Bachem, O.

8th International Conference on Learning Representations (ICLR), April 2020 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


no image
Mixed-curvature Variational Autoencoders

Skopek, O., Ganea, O., Becigneul, G.

8th International Conference on Learning Representations (ICLR), April 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals
Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals

Laumann, F., von Kügelgen, J., Barahona, M.

ICLR 2020 Workshop "Tackling Climate Change with Machine Learning", April 2020 (conference)

ei

arXiv PDF [BibTex]

arXiv PDF [BibTex]


From Variational to Deterministic Autoencoders
From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

8th International Conference on Learning Representations (ICLR) , April 2020, *equal contribution (conference) Accepted

Abstract
Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

ei ps

arXiv [BibTex]

arXiv [BibTex]


no image
On Mutual Information Maximization for Representation Learning

Tschannen, M., Djolonga, J., Rubenstein, P. K., Gelly, S., Lucic, M.

8th International Conference on Learning Representations (ICLR), April 2020 (conference) Accepted

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


Towards causal generative scene models via competition of experts
Towards causal generative scene models via competition of experts

von Kügelgen*, J., Ustyuzhaninov*, I., Gehler, P., Bethge, M., Schölkopf, B.

ICLR 2020 Workshop "Causal Learning for Decision Making", April 2020, *equal contribution (conference)

ei

arXiv PDF [BibTex]

arXiv PDF [BibTex]


no image
More Powerful Selective Kernel Tests for Feature Selection

Lim, J. N., Yamada, M., Jitkrittum, W., Terada, Y., Matsui, S., Shimodaira, H.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 (conference) To be published

ei

arXiv [BibTex]

arXiv [BibTex]


no image
Computationally Tractable Riemannian Manifolds for Graph Embeddings

Cruceru, C., Becigneul, G., Ganea, O.

37th International Conference on Machine Learning (ICML), 2020 (conference) Submitted

ei

[BibTex]

[BibTex]


no image
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models

Agudelo-España, D., Zadaianchuk, A., Wenk, P., Garg, A., Akpo, J., Grimminger, F., Viereck, J., Naveau, M., Righetti, L., Martius, G., Krause, A., Schölkopf, B., Bauer, S., Wüthrich, M.

IEEE International Conference on Robotics and Automation (ICRA), 2020 (conference) Accepted

am al ei mg

Project Page PDF [BibTex]

Project Page PDF [BibTex]


no image
Practical Accelerated Optimization on Riemannian Manifolds

F Alimisis, F., Orvieto, A., Becigneul, G., Lucchi, A.

37th International Conference on Machine Learning (ICML), 2020 (conference) Submitted

ei

[BibTex]

[BibTex]


no image
Fair Decisions Despite Imperfect Predictions

Kilbertus, N., Gomez Rodriguez, M., Schölkopf, B., Muandet, K., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 (conference) Accepted

ei plg

[BibTex]

[BibTex]


no image
Planning from Images with Deep Latent Gaussian Process Dynamics

Bosch, N., Achterhold, J., Leal-Taixe, L., Stückler, J.

2nd Annual Conference on Learning for Dynamics and Control (L4DC) , 2020, to appear, arXiv:2005.03770 (conference) Accepted

ev

preprint project page poster [BibTex]

preprint project page poster [BibTex]


no image
Constant Curvature Graph Convolutional Networks

Bachmann*, G., Becigneul*, G., Ganea, O.

37th International Conference on Machine Learning (ICML), 2020, *equal contribution (conference) Submitted

ei

[BibTex]

[BibTex]


Optimizing Rank-based Metrics with Blackbox Differentiation
Optimizing Rank-based Metrics with Blackbox Differentiation

Rolinek, M., Musil, V., Paulus, A., Vlastelica, M., Michaelis, C., Martius, G.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020, Best paper nomination (inproceedings)

Abstract
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we address optimization instability and sparsity of the supervision signal that both arise from using rank-based metrics as optimization targets. Resulting losses based on recall and Average Precision are applied to image retrieval and object detection tasks. We obtain performance that is competitive with state-of-the-art on standard image retrieval datasets and consistently improve performance of near state-of-the-art object detectors.

al

Code Long Oral Short Oral Arxiv Project Page [BibTex]

Code Long Oral Short Oral Arxiv Project Page [BibTex]


no image
DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation

Wang, R., Yang, N., Stückler, J., Cremers, D.

In Accepted for IEEE international Conference on Robotics and Automation (ICRA), 2020, arXiv:1904.10097 (inproceedings) Accepted

ev

[BibTex]

[BibTex]


no image
Divide-and-Conquer Monte Carlo Tree Search for goal directed planning

Parascandolo*, G., Buesing*, L., Merel, J., Hasenclever, L., Aslanides, J., Hamrick, J. B., Heess, N., Neitz, A., Weber, T.

2020, *equal contribution (conference) Submitted

ei

arXiv [BibTex]

arXiv [BibTex]

2008


no image
Stereo Matching for Calibrated Cameras without Correspondence

Helmke, U., Hüper, K., Vences, L.

In CDC 2008, pages: 2408-2413, IEEE Service Center, Piscataway, NJ, USA, 47th IEEE Conference on Decision and Control, December 2008 (inproceedings)

Abstract
We study the stereo matching problem for reconstruction of the location of 3D-points on an unknown surface patch from two calibrated identical cameras without using any a priori information about the pointwise correspondences. We assume that camera parameters and the pose between the cameras are known. Our approach follows earlier work for coplanar cameras where a gradient flow algorithm was proposed to match associated Gramians. Here we extend this method by allowing arbitrary poses for the cameras. We introduce an intrinsic Riemannian Newton algorithm that achieves local quadratic convergence rates. A closed form solution is presented, too. The efficiency of both algorithms is demonstrated by numerical experiments.

ei

PDF Web DOI [BibTex]

2008


PDF Web DOI [BibTex]


no image
Joint Kernel Support Estimation for Structured Prediction

Lampert, C., Blaschko, M.

In Proceedings of the NIPS 2008 Workshop on "Structured Input - Structured Output" (NIPS SISO 2008), pages: 1-4, NIPS Workshop on "Structured Input - Structured Output" (NIPS SISO), December 2008 (inproceedings)

Abstract
We present a new technique for structured prediction that works in a hybrid generative/ discriminative way, using a one-class support vector machine to model the joint probability of (input, output)-pairs in a joint reproducing kernel Hilbert space. Compared to discriminative techniques, like conditional random elds or structured out- put SVMs, the proposed method has the advantage that its training time depends only on the number of training examples, not on the size of the label space. Due to its generative aspect, it is also very tolerant against ambiguous, incomplete or incorrect labels. Experiments on realistic data show that our method works eciently and robustly in situations for which discriminative techniques have computational or statistical problems.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Frequent Subgraph Retrieval in Geometric Graph Databases

Nowozin, S., Tsuda, K.

In ICDM 2008, pages: 953-958, (Editors: Giannotti, F. , D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, X. Wu), IEEE Computer Society, Los Alamitos, CA, USA, 8th IEEE International Conference on Data Mining, December 2008 (inproceedings)

Abstract
Discovery of knowledge from geometric graph databases is of particular importance in chemistry and biology, because chemical compounds and proteins are represented as graphs with 3D geometric coordinates. In such applications, scientists are not interested in the statistics of the whole database. Instead they need information about a novel drug candidate or protein at hand, represented as a query graph. We propose a polynomial-delay algorithm for geometric frequent subgraph retrieval. It enumerates all subgraphs of a single given query graph which are frequent geometric $epsilon$-subgraphs under the entire class of rigid geometric transformations in a database. By using geometric$epsilon$-subgraphs, we achieve tolerance against variations in geometry. We compare the proposed algorithm to gSpan on chemical compound data, and we show that for a given minimum support the total number of frequent patterns is substantially limited by requiring geometric matching. Although the computation time per pattern is lar ger than for non-geometric graph mining,the total time is within a reasonable level even for small minimum support.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Block Iterative Algorithms for Non-negative Matrix Approximation

Sra, S.

In ICDM 2008, pages: 1037-1042, (Editors: Giannotti, F. , D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, X. Wu), IEEE Service Center, Piscataway, NJ, USA, Eighth IEEE International Conference on Data Mining, December 2008 (inproceedings)

Abstract
In this paper we present new algorithms for non-negative matrix approximation (NMA), commonly known as the NMF problem. Our methods improve upon the well-known methods of Lee & Seung~cite{lee00} for both the Frobenius norm as well the Kullback-Leibler divergence versions of the problem. For the latter problem, our results are especially interesting because it seems to have witnessed much lesser algorithmic progress as compared to the Frobenius norm NMA problem. Our algorithms are based on a particular textbf {block-iterative} acceleration technique for EM, which preserves the multiplicative nature of the updates and also ensures monotonicity. Furthermore, our algorithms also naturally apply to the Bregman-divergence NMA algorithms of~cite{suv.nips}. Experimentally, we show that our algorithms outperform the traditional Lee/Seung approach most of the time.

ei

Web DOI [BibTex]

Web DOI [BibTex]


no image
A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation

Chiappa, S.

In ICMLA 2008, pages: 3-9, (Editors: Wani, M. A., X.-W. Chen, D. Casasent, L. Kurgan, T. Hu, K. Hafeez), IEEE Computer Society, Los Alamitos, CA, USA, 7th International Conference on Machine Learning and Applications, December 2008 (inproceedings)

Abstract
Time-series segmentation in the fully unsupervised scenario in which the number of segment-types is a priori unknown is a fundamental problem in many applications. We propose a Bayesian approach to a segmentation model based on the switching linear Gaussian state-space model that enforces a sparse parametrization, such as to use only a small number of a priori available different dynamics to explain the data. This enables us to estimate the number of segment-types within the model, in contrast to previous non-Bayesian approaches where training and comparing several separate models was required. As the resulting model is computationally intractable, we introduce a variational approximation where a reformulation of the problem enables the use of efficient inference algorithms.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Iterative Subgraph Mining for Principal Component Analysis

Saigo, H., Tsuda, K.

In ICDM 2008, pages: 1007-1012, (Editors: Giannotti, F. , D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, X. Wu), IEEE Computer Society, Los Alamitos, CA, USA, IEEE International Conference on Data Mining, December 2008 (inproceedings)

Abstract
Graph mining methods enumerate frequent subgraphs efficiently, but they are not necessarily good features for machine learning due to high correlation among features. Thus it makes sense to perform principal component analysis to reduce the dimensionality and create decorrelated features. We present a novel iterative mining algorithm that captures informative patterns corresponding to major entries of top principal components. It repeatedly calls weighted substructure mining where example weights are updated in each iteration. The Lanczos algorithm, a standard algorithm of eigendecomposition, is employed to update the weights. In experiments, our patterns are shown to approximate the principal components obtained by frequent mining.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Probabilistic Inference for Fast Learning in Control

Rasmussen, CE., Deisenroth, MP.

In EWRL 2008, pages: 229-242, (Editors: Girgin, S. , M. Loth, R. Munos, P. Preux, D. Ryabko), Springer, Berlin, Germany, 8th European Workshop on Reinforcement Learning, November 2008 (inproceedings)

Abstract
We provide a novel framework for very fast model-based reinforcement learning in continuous state and action spaces. The framework requires probabilistic models that explicitly characterize their levels of confidence. Within this framework, we use flexible, non-parametric models to describe the world based on previously collected experience. We demonstrate learning on the cart-pole problem in a setting where we provide very limited prior knowledge about the task. Learning progresses rapidly, and a good policy is found after only a hand-full of iterations.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Policy Learning: A Unified Perspective with Applications in Robotics

Peters, J., Kober, J., Nguyen-Tuong, D.

In EWRL 2008, pages: 220-228, (Editors: Girgin, S. , M. Loth, R. Munos, P. Preux, D. Ryabko), Springer, Berlin, Germany, 8th European Workshop on Reinforcement Learning, November 2008 (inproceedings)

Abstract
Policy Learning approaches are among the best suited methods for high-dimensional, continuous control systems such as anthropomorphic robot arms and humanoid robots. In this paper, we show two contributions: firstly, we show a unified perspective which allows us to derive several policy learning algorithms from a common point of view, i.e, policy gradient algorithms, natural-gradient algorithms and EM-like policy learning. Secondly, we present several applications to both robot motor primitive learning as well as to robot control in task space. Results both from simulation and several different real robots are shown.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Learning to Localize Objects with Structured Output Regression

Blaschko, MB., Lampert, CH.

In ECCV 2008, pages: 2-15, (Editors: Forsyth, D. A., P. H.S. Torr, A. Zisserman), Springer, Berlin, Germany, 10th European Conference on Computer Vision, October 2008, Best Student Paper Award (inproceedings)

Abstract
Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization task. First a binary classifier is trained using a sample of positive and negative examples, and this classifier is subsequently applied to multiple regions within test images. We propose instead to treat object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the prediction of the bounding box of objects located in images. The use of a joint-kernel framework allows us to formulate the training procedure as a generalization of an SVM, which can be solved efficiently. We further improve computational efficiency by using a branch-and-bound strategy for localization during both training and testing. Experimental evaluation on the PASCAL VOC and TU Darmstadt datasets show that the structured training procedure improves pe rformance over binary training as well as the best previously published scores.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Automatic Image Colorization Via Multimodal Predictions

Charpiat, G., Hofmann, M., Schölkopf, B.

In Computer Vision - ECCV 2008, Lecture Notes in Computer Science, Vol. 5304, pages: 126-139, (Editors: DA Forsyth and PHS Torr and A Zisserman), Springer, Berlin, Germany, 10th European Conference on Computer Vision, October 2008 (inproceedings)

Abstract
We aim to color automatically greyscale images, 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 nonuniform 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.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Nonparametric Independence Tests: Space Partitioning and Kernel Approaches

Gretton, A., Györfi, L.

In ALT08, pages: 183-198, (Editors: Freund, Y. , L. Györfi, G. Turán, T. Zeugmann), Springer, Berlin, Germany, 19th International Conference on Algorithmic Learning Theory (ALT08), October 2008 (inproceedings)

Abstract
Three simple and explicit procedures for testing the independence of two multi-dimensional random variables are described. Two of the associated test statistics (L1, log-likelihood) are defined when the empirical distribution of the variables is restricted to finite partitions. A third test statistic is defined as a kernel-based independence measure. All tests reject the null hypothesis of independence if the test statistics become large. The large deviation and limit distribution properties of all three test statistics are given. Following from these results, distributionfree strong consistent tests of independence are derived, as are asymptotically alpha-level tests. The performance of the tests is evaluated experimentally on benchmark data.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Automatic 3D Face Reconstruction from Single Images or Video

Breuer, P., Kim, K., Kienzle, W., Schölkopf, B., Blanz, V.

In FG 2008, pages: 1-8, IEEE Computer Society, Los Alamitos, CA, USA, 8th IEEE International Conference on Automatic Face and Gesture Recognition, September 2008 (inproceedings)

Abstract
This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression- and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. To make the algorithm robust with respect to head orientation, this process is iterated while the estimate of pose is refined. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a highresolution 3D surface model.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Kernel Measures of Conditional Dependence

Fukumizu, K., Gretton, A., Sun, X., Schölkopf, B.

In Advances in neural information processing systems 20, pages: 489-496, (Editors: JC Platt and D Koller and Y Singer and S Roweis), Curran, Red Hook, NY, USA, 21st Annual Conference on Neural Information Processing Systems (NIPS), September 2008 (inproceedings)

Abstract
We propose a new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces. Unlike previous kernel dependence measures, the proposed criterion does not depend on the choice of kernel in the limit of infinite data, for a wide class of kernels. At the same time, it has a straightforward empirical estimate with good convergence behaviour. We discuss the theoretical properties of the measure, and demonstrate its application in experiments.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
An Analysis of Inference with the Universum

Sinz, F., Chapelle, O., Agarwal, A., Schölkopf, B.

In Advances in neural information processing systems 20, pages: 1369-1376, (Editors: JC Platt and D Koller and Y Singer and S Roweis), Curran, Red Hook, NY, USA, 21st Annual Conference on Neural Information Processing Systems (NIPS), September 2008 (inproceedings)

Abstract
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative data, a third class of data is available, termed the Universum. We assay the behavior of the algorithm by establishing links with Fisher discriminant analysis and oriented PCA, as well as with an SVM in a projected subspace (or, equivalently, with a data-dependent reduced kernel). We also provide experimental results.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Learning with Transformation Invariant Kernels

Walder, C., Chapelle, O.

In Advances in neural information processing systems 20, pages: 1561-1568, (Editors: Platt, J. C., D. Koller, Y. Singer, S. Roweis), Curran, Red Hook, NY, USA, Twenty-First Annual Conference on Neural Information Processing Systems (NIPS), September 2008 (inproceedings)

Abstract
This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation invariant, only conditionally positive definite (c.p.d.) ones. Accordingly, we discuss the c.p.d. case and provide some novel analysis, including an elementary derivation of a c.p.d. representer theorem. On the practical side, we give a support vector machine (s.v.m.) algorithm for arbitrary c.p.d. kernels. For the thinplate kernel this leads to a classifier with only one parameter (the amount of regularisation), which we demonstrate to be as effective as an s.v.m. with the Gaussian kernel, even though the Gaussian involves a second parameter (the length scale).

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Episodic Reinforcement Learning by Logistic Reward-Weighted Regression

Wierstra, D., Schaul, T., Peters, J., Schmidhuber, J.

In ICANN 2008, pages: 407-416, (Editors: Kurkova-Pohlova, V. , R. Neruda, J. Koutnik), Springer, Berlin, Germany, 18th International Conference on Artificial Neural Networks, September 2008 (inproceedings)

Abstract
It has been a long-standing goal in the adaptive control community to reduce the generically difficult, general reinforcement learning (RL) problem to simpler problems solvable by supervised learning. While this approach is today’s standard for value function-based methods, fewer approaches are known that apply similar reductions to policy search methods. Recently, it has been shown that immediate RL problems can be solved by reward-weighted regression, and that the resulting algorithm is an expectation maximization (EM) algorithm with strong guarantees. In this paper, we extend this algorithm to the episodic case and show that it can be used in the context of LSTM recurrent neural networks (RNNs). The resulting RNN training algorithm is equivalent to a weighted self-modeling supervised learning technique. We focus on partially observable Markov decision problems (POMDPs) where it is essential that the policy is nonstationary in order to be optimal. We show that this new reward-weighted logistic regression u sed in conjunction with an RNN architecture can solve standard benchmark POMDPs with ease.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Local Gaussian Processes Regression for Real-time Model-based Robot Control

Nguyen-Tuong, D., Peters, J.

In IROS 2008, pages: 380-385, IEEE Service Center, Piscataway, NJ, USA, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 2008 (inproceedings)

Abstract
High performance and compliant robot control require accurate dynamics models which 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. This approach offers a natural framework to incorporate unknown nonlinearities as well as to continually adapt online for changes in the robot dynamics. However, the most accurate regression methods, e.g. Gaussian processes regression (GPR) and support vector regression (SVR), suffer from exceptional high computational complexity which prevents their usage for large numbers of samples or online learning to date. Inspired by locally linear regression techniques, we propose an approximation to the standard GPR using local Gaussian processes models. 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, nu-SVR and locally weighted projection regression (LWPR), show that LGP has higher accuracy than LWPR close to the performance of standard GPR and nu-SVR while being sufficiently fast for online learning.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Colored Maximum Variance Unfolding

Song, L., Smola, A., Borgwardt, K., Gretton, A.

In Advances in neural information processing systems 20, pages: 1385-1392, (Editors: Platt, J. C., D. Koller, Y. Singer, S. Roweis), Curran, Red Hook, NY, USA, Twenty-First Annual Conference on Neural Information Processing Systems (NIPS), September 2008 (inproceedings)

Abstract
Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of their embeddings while preserving the local distances of the original data. We show that MVU also optimizes a statistical dependence measure which aims to retain the identity of individual observations under the distancepreserving constraints. This general view allows us to design "colored" variants of MVU, which produce low-dimensional representations for a given task, e.g. subject to class labels or other side information.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Assessing Nonlinear Granger Causality from Multivariate Time Series

Sun, X.

In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, pages: 440-455, (Editors: Daelemans, W. , B. Goethals, K. Morik), Springer, Berlin, Germany, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), September 2008 (inproceedings)

Abstract
A straightforward nonlinear extension of Granger’s concept of causality in the kernel framework is suggested. The kernel-based approach to assessing nonlinear Granger causality in multivariate time series enables us to determine, in a model-free way, whether the causal relation between two time series is present or not and whether it is direct or mediated by other processes. The trace norm of the so-called covariance operator in feature space is used to measure the prediction error. Relying on this measure, we test the improvement of predictability between time series by subsampling-based multiple testing. The distributional properties of the resulting p-values reveal the direction of Granger causality. Experiments with simulated and real-world data show that our method provides encouraging results.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


no image
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images

Bethge, M., Berens, P.

In Advances in neural information processing systems 20, pages: 97-104, (Editors: Platt, J. C., D. Koller, Y. Singer, S. Roweis), Curran, Red Hook, NY, USA, Twenty-First Annual Conference on Neural Information Processing Systems (NIPS), September 2008 (inproceedings)

Abstract
Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer from their poor scalability to high dimensions. In sensory coding, however, high-dimensional data is ubiquitous. Here, we introduce a new approach using a near-maximum entropy model, that makes this type of analysis feasible for very high-dimensional data - the model parameters can be derived in closed form and sampling is easy. We demonstrate its usefulness by studying a simple neural representation model of natural images. For the first time, we are able to directly compare predictions from a pairwise maximum entropy model not only in small groups of neurons, but also in larger populations of more than thousand units. Our results indicate that in such larger networks interactions exist that are not predicted by pairwise correlations, despite the fact that pairwise correlations explain the lower-dimensional marginal statistics extrem ely well up to the limit of dimensionality where estimation of the full joint distribution is feasible.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Learning Perceptual Coupling for Motor Primitives

Kober, J., Mohler, B., Peters, J.

In IROS 2008, pages: 834-839, IEEE Service Center, Piscataway, NJ, USA, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, September 2008 (inproceedings)

Abstract
Dynamic system-based motor primitives [1] have enabled robots to learn complex tasks ranging from Tennisswings to locomotion. However, to date there have been only few extensions which have incorporated perceptual coupling to variables of external focus, and, furthermore, these modifications have relied upon handcrafted solutions. Humans learn how to couple their movement primitives with external variables. Clearly, such a solution is needed in robotics. In this paper, we propose an augmented version of the dynamic systems motor primitives which incorporates perceptual coupling to an external variable. The resulting perceptually driven motor primitives include the previous primitives as a special case and can inherit some of their interesting properties. 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. For doing so, we initialize the motor primitives in the traditional way by imitation learning without perceptual coupling. Subsequently, we improve the motor primitives using a novel reinforcement learning method which is particularly well-suited for motor primitives.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Receptive Fields without Spike-Triggering

Macke, J., Zeck, G., Bethge, M.

In Advances in neural information processing systems 20, pages: 969-976, (Editors: Platt, J. C., D. Koller, Y. Singer, S. Roweis), Curran, Red Hook, NY, USA, Twenty-First Annual Conference on Neural Information Processing Systems (NIPS), September 2008 (inproceedings)

Abstract
Stimulus selectivity of sensory neurons is often characterized by estimating their receptive field properties such as orientation selectivity. Receptive fields are usually derived from the mean (or covariance) of the spike-triggered stimulus ensemble. This approach treats each spike as an independent message but does not take into account that information might be conveyed through patterns of neural activity that are distributed across space or time. Can we find a concise description for the processing of a whole population of neurons analogous to the receptive field for single neurons? Here, we present a generalization of the linear receptive field which is not bound to be triggered on individual spikes but can be meaningfully linked to distributed response patterns. More precisely, we seek to identify those stimulus features and the corresponding patterns of neural activity that are most reliably coupled. We use an extension of reverse-correlation methods based on canonical correlation analysis. The resulting population receptive fields span the subspace of stimuli that is most informative about the population response. We evaluate our approach using both neuronal models and multi-electrode recordings from rabbit retinal ganglion cells. We show how the model can be extended to capture nonlinear stimulus-response relationships using kernel canonical correlation analysis, which makes it possible to test different coding mechanisms. Our technique can also be used to calculate receptive fields from multi-dimensional neural measurements such as those obtained from dynamic imaging methods.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
An Automated Combination of Kernels for Predicting Protein Subcellular Localization

Ong, C., Zien, A.

In WABI 2008, pages: 186-197, (Editors: Crandall, K. A., J. Lagergren), Springer, Berlin, Germany, 8th Workshop on Algorithms in Bioinformatics, September 2008 (inproceedings)

Abstract
Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. While many predictive computational tools have been proposed, they tend to have complicated architectures and require many design decisions from the developer. Here we utilize the multiclass support vector machine (m-SVM) method to directly solve protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. We further propose a general class of protein sequence kernels which considers all motifs, including motifs with gaps. Instead of heuristically selecting one or a few kernels from this family, we utilize a recent extension of SVMs that optimizes over multiple kernels simultaneously. This way, we automatically search over families of possible amino acid motifs. We compare our automated approach to three other predictors on four different datasets, and show that we perform better than the current state of the art. Further, our method provides some insights as to which sequence motifs are most useful for determining subcellular ocalization, which are in agreement with biological reasoning.

ei

PDF PDF Web DOI [BibTex]

PDF PDF Web DOI [BibTex]


no image
Discriminative K-means for Clustering

Ye, J., Zhao, Z., Wu, M.

In Advances in neural information processing systems 20, pages: 1649-1656, (Editors: Platt, J. C., D. Koller, Y. Singer, S. Roweis), Curran, Red Hook, NY, USA, Twenty-First Annual Conference on Neural Information Processing Systems (NIPS), September 2008 (inproceedings)

Abstract
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and clustering. Empirical results have shown its favorable performance in comparison with several other popular clustering algorithms. However, the inherent relationship between subspace selection and clustering in this framework is not well understood, due to the iterative nature of the algorithm. We show in this paper that this iterative subspace selection and clustering is equivalent to kernel K-means with a specific kernel Gram matrix. This provides significant and new insights into the nature of this subspace selection procedure. Based on this equivalence relationship, we propose the Discriminative K-means (DisKmeans) algorithm for simultaneous LDA subspace selection and clustering, as well as an automatic parameter estimation procedure. We also present the nonlinear extension of DisKmeans using kernels. We show that the learning of the ke rnel matrix over a convex set of pre-specified kernel matrices can be incorporated into the clustering formulation. The connection between DisKmeans and several other clustering algorithms is also analyzed. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets.

ei

PDF Web [BibTex]

PDF Web [BibTex]