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2020


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Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Karimi*, A., von Kügelgen*, J., Schölkopf, B., Valera, I.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020, *equal contribution (conference) Accepted

arXiv [BibTex]

2020

arXiv [BibTex]


Grasping Field: Learning Implicit Representations for Human Grasps
Grasping Field: Learning Implicit Representations for Human Grasps

Karunratanakul, K., Yang, J., Zhang, Y., Black, M., Muandet, K., Tang, S.

In International Conference on 3D Vision (3DV), November 2020 (inproceedings)

Abstract
Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface of the object; and (3) it should interact with the object in a semantically and physically plausible manner. To make progress in this direction, we draw inspiration from the recent progress on learning-based implicit representations for 3D object reconstruction. Specifically, we propose an expressive representation for human grasp modelling that is efficient and easy to integrate with deep neural networks. Our insight is that every point in a three-dimensional space can be characterized by the signed distances to the surface of the hand and the object, respectively. Consequently, the hand, the object, and the contact area can be represented by implicit surfaces in a common space, in which the proximity between the hand and the object can be modelled explicitly. We name this 3D to 2D mapping as Grasping Field, parameterize it with a deep neural network, and learn it from data. We demonstrate that the proposed grasping field is an effective and expressive representation for human grasp generation. Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud. The extensive experiments demonstrate that our generative model compares favorably with a strong baseline and approaches the level of natural human grasps. Furthermore, based on the grasping field representation, we propose a deep network for the challenging task of 3D hand-object interaction reconstruction from a single RGB image. Our method improves the physical plausibility of the hand-object contact reconstruction and achieves comparable performance for 3D hand reconstruction compared to state-of-the-art methods. Our model and code are available for research purpose at https://github.com/korrawe/grasping_field.

pdf arXiv code [BibTex]


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MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Hohmann, M. R., Konieczny, L., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST), October 2020 (conference) Accepted

arXiv DOI [BibTex]

arXiv DOI [BibTex]


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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), 108, pages: 895-905, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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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), 108, pages: 820-830, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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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), 124, pages: 320-329, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Semi-supervised learning, causality, and the conditional cluster assumption

von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI) , 124, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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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), 124, pages: 41-50, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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On the design of consequential ranking algorithms

Tabibian, B., Gómez, V., De, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 171-180, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Importance Sampling via Local Sensitivity

Raj, A., Musco, C., Mackey, L.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 3099-3109, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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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), 108, pages: 1297-1307, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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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), 108, pages: 277-287, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Integrals over Gaussians under Linear Domain Constraints

Gessner, A., Kanjilal, O., Hennig, P.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 2764-2774, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Modular Block-diagonal Curvature Approximations for Feedforward Architectures

Dangel, F., Harmeling, S., Hennig, P.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 799-808, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Testing Goodness of Fit of Conditional Density Models with Kernels

Jitkrittum, W., Kanagawa, H., Schölkopf, B.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 221-230, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization

Negiar, G., Dresdner, G., Tsai, A. Y., El Ghaoui, L., Locatello, F., Freund, R. M., Pedregosa, F.

37th International Conference on Machine Learning (ICML), pages: 296-305, July 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Variational Autoencoders with Riemannian Brownian Motion Priors

Kalatzis, D., Eklund, D., Arvanitidis, G., Hauberg, S.

37th International Conference on Machine Learning (ICML), pages: 6789-6799, July 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Variational Bayes in Private Settings (VIPS) (Extended Abstract)

Foulds, J. R., Park, M., Chaudhuri, K., Welling, M.

Proceedings of the 29th International Joint Conference on Artificial Intelligence, (IJCAI-PRICAI), pages: 5050-5054, (Editors: Christian Bessiere), International Joint Conferences on Artificial Intelligence Organization, July 2020, Journal track (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Weakly-Supervised Disentanglement Without Compromises

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

37th International Conference on Machine Learning (ICML), pages: 7753-7764, July 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Constant Curvature Graph Convolutional Networks

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

37th International Conference on Machine Learning (ICML), pages: 9118-9128, July 2020, *equal contribution (conference)

link (url) [BibTex]

link (url) [BibTex]


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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Kristiadi, A., Hein, M., Hennig, P.

37th International Conference on Machine Learning (ICML), pages: 1226-1236, July 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Differentiable Likelihoods for Fast Inversion of ‘Likelihood-Free’ Dynamical Systems

Kersting, H., Krämer, N., Schiegg, M., Daniel, C., Tiemann, M., Hennig, P.

37th International Conference on Machine Learning (ICML), pages: 2655-2665, July 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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Kernel Conditional Density Operators

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

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 993-1004, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, June 2020 (conference)

link (url) [BibTex]

link (url) [BibTex]


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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), 120, pages: 915-923, Proceedings of Machine Learning Research, (Editors: Alexandre M. Bayen and Ali Jadbabaie and George Pappas and Pablo A. Parrilo and Benjamin Recht and Claire Tomlin and Melanie Zeilinger), PMLR, June 2020 (conference)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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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)

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Mixed-curvature Variational Autoencoders

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

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

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)

arXiv PDF [BibTex]

arXiv PDF [BibTex]


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Counterfactuals uncover the modular structure of deep generative models

Besserve, M., Mehrjou, A., Sun, R., Schölkopf, B.

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

link (url) [BibTex]

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)

arXiv PDF [BibTex]

arXiv PDF [BibTex]


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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)

arXiv link (url) [BibTex]

arXiv link (url) [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)

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.

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Radial and Directional Posteriors for Bayesian Deep Learning

Oh, C., Adamczewski, K., Park, M.

Proceedings of the 34th Conference on Artificial Intelligence (AAAI), 34(4):5298-5305, AAAI Press, Febuary 2020, AAAI Technical Track: Machine Learning (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

Wenk, P., Abbati, G., Osborne, M. A., Schölkopf, B., Krause, A., Bauer, S.

Proceedings of the 34th Conference on Artificial Intelligence (AAAI), 34(4):6364-6371, AAAI Press, Febuary 2020, AAAI Technical Track: Machine Learning (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Interpretable and Differentially Private Predictions

Harder, F., Bauer, M., Park, M.

Proceedings of the 34th Conference on Artificial Intelligence (AAAI), 34(4):4083-4090, AAAI Press, Febuary 2020, AAAI Technical Track: Machine Learning (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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A Commentary on the Unsupervised Learning of Disentangled Representations

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

Proceedings of the 34th Conference on Artificial Intelligence (AAAI), 34(9):13681-13684, AAAI Press, Febuary 2020, Sister Conference Track (conference)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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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

Project Page PDF [BibTex]

Project Page PDF [BibTex]


Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem
Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem

Zhu, J., Jitkrittum, W., Diehl, M., Schölkopf, B.

In 59th IEEE Conference on Decision and Control (CDC), 2020 (inproceedings) Accepted

[BibTex]

[BibTex]


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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

arXiv [BibTex]

arXiv [BibTex]

2005


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Kernel ICA for Large Scale Problems

Jegelka, S., Gretton, A., Achlioptas, D.

In pages: -, NIPS Workshop on Large Scale Kernel Machines, December 2005 (inproceedings)

Web [BibTex]

2005

Web [BibTex]


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Infinite dimensional exponential families by reproducing kernel Hilbert spaces

Fukumizu, K.

In IGAIA 2005, pages: 324-333, 2nd International Symposium on Information Geometry and its Applications, December 2005 (inproceedings)

Abstract
The purpose of this paper is to propose a method of constructing exponential families of Hilbert manifold, on which estimation theory can be built. Although there have been works on infinite dimensional exponential families of Banach manifolds (Pistone and Sempi, 1995; Gibilisco and Pistone, 1998; Pistone and Rogantin, 1999), they are not appropriate to discuss statistical estimation with finite number of samples; the likelihood function with finite samples is not continuous on the manifold. In this paper we use a reproducing kernel Hilbert space as a functional space for constructing an exponential manifold. A reproducing kernel Hilbert space is dened as a Hilbert space of functions such that evaluation of a function at an arbitrary point is a continuous functional on the Hilbert space. Since we can discuss the value of a function with this space, it is very natural to use a manifold associated with a reproducing kernel Hilbert space as a basis of estimation theory. We focus on the maximum likelihood estimation (MLE) with the exponential manifold of a reproducing kernel Hilbert space. As in many non-parametric estimation methods, straightforward extension of MLE to an infinite dimensional exponential manifold suffers the problem of ill-posedness caused by the fact that the estimator should be chosen from the infinite dimensional space with only finite number of constraints given by the data. To solve this problem, a pseudo-maximum likelihood method is proposed by restricting the infinite dimensional manifold to a series of finite dimensional submanifolds, which enlarge as the number of samples increases. Some asymptotic results in the limit of infinite samples are shown, including the consistency of the pseudo-MLE.

PDF Web [BibTex]

PDF Web [BibTex]


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Shortest-path kernels on graphs

Borgwardt, KM., Kriegel, H-P.

In pages: 74-81, IEEE Computer Society, Los Alamitos, CA, USA, Fifth International Conference on Data Mining (ICDM), November 2005 (inproceedings)

Abstract
Data mining algorithms are facing the challenge to deal with an increasing number of complex objects. For graph data, a whole toolbox of data mining algorithms becomes available by defining a kernel function on instances of graphs. Graph kernels based on walks, subtrees and cycles in graphs have been proposed so far. As a general problem, these kernels are either computationally expensive or limited in their expressiveness. We try to overcome this problem by defining expressive graph kernels which are based on paths. As the computation of all paths and longest paths in a graph is NP-hard, we propose graph kernels based on shortest paths. These kernels are computable in polynomial time, retain expressivity and are still positive definite. In experiments on classification of graph models of proteins, our shortest-path kernels show significantly higher classification accuracy than walk-based kernels.

Web DOI [BibTex]

Web DOI [BibTex]


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Training Support Vector Machines with Multiple Equality Constraints

Kienzle, W., Schölkopf, B.

In Proceedings of the 16th European Conference on Machine Learning, Lecture Notes in Computer Science, Vol. 3720, pages: 182-193, (Editors: JG Carbonell and J Siekmann), Springer, Berlin, Germany, ECML, November 2005 (inproceedings)

Abstract
In this paper we present a primal-dual decomposition algorithm for support vector machine training. As with existing methods that use very small working sets (such as Sequential Minimal Optimization (SMO), Successive Over-Relaxation (SOR) or the Kernel Adatron (KA)), our method scales well, is straightforward to implement, and does not require an external QP solver. Unlike SMO, SOR and KA, the method is applicable to a large number of SVM formulations regardless of the number of equality constraints involved. The effectiveness of our algorithm is demonstrated on a more difficult SVM variant in this respect, namely semi-parametric support vector regression.

PDF DOI [BibTex]

PDF DOI [BibTex]


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Measuring Statistical Dependence with Hilbert-Schmidt Norms

Gretton, A., Bousquet, O., Smola, A., Schoelkopf, B.

In Algorithmic Learning Theory, Lecture Notes in Computer Science, Vol. 3734, pages: 63-78, (Editors: S Jain and H-U Simon and E Tomita), Springer, Berlin, Germany, 16th International Conference ALT, October 2005 (inproceedings)

Abstract
We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on {methodname} do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

PDF DOI [BibTex]

PDF DOI [BibTex]


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An Analysis of the Anti-Learning Phenomenon for the Class Symmetric Polyhedron

Kowalczyk, A., Chapelle, O.

In Algorithmic Learning Theory: 16th International Conference, pages: 78-92, Algorithmic Learning Theory, October 2005 (inproceedings)

Abstract
This paper deals with an unusual phenomenon where most machine learning algorithms yield good performance on the training set but systematically worse than random performance on the test set. This has been observed so far for some natural data sets and demonstrated for some synthetic data sets when the classification rule is learned from a small set of training samples drawn from some high dimensional space. The initial analysis presented in this paper shows that anti-learning is a property of data sets and is quite distinct from overfitting of a training data. Moreover, the analysis leads to a specification of some machine learning procedures which can overcome anti-learning and generate ma- chines able to classify training and test data consistently.

PDF [BibTex]

PDF [BibTex]


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A new methodology for robot controller design

Peters, J., Peters, J., Mistry, M., Udwadia, F.

In Proceedings of the 5th ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC‘05), 5, pages: 1067-1076 , ASME, New York, NY, USA, 5th ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-MSNDC), September 2005 (inproceedings)

Abstract
Gauss' principle of least constraint and its generalizations have provided a useful insights for the development of tracking controllers for mechanical systems [1]. Using this concept, we present a novel methodology for the design of a specific class of robot controllers. With our new framework, we demonstrate that well-known and also several novel nonlinear robot control laws can be derived from this generic framework, and show experimental verifications on a Sarcos Master Arm robot for some of these controllers. We believe that the suggested approach unifies and simplifies the design of optimal nonlinear control laws for robots obeying rigid body dynamics equations, both with or without external constraints, holonomic or nonholonomic constraints, with over-actuation or underactuation, as well as open-chain and closed-chain kinematics.

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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EEG-Based Mental Task Classification: Linear and Nonlinear Classification of Movement Imagery

Athena Akrami, A.

In EMBS, 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), September 1-4,, Shanghai, China (Accepted), September 2005 (inproceedings) Accepted

Abstract
Abstract—Use of EEG signals as a channel of communication between men and machines represents one of the current challenges in signal theory research. The principal element of such a communication system, known as a “Brain-Computer Interface,” is the interpretation of the EEG signals related to the characteristic parameters of brain electrical activity. Our goal in this work was extracting quantitative changes in the EEG due to movement imagination. Subject‘s EEG was recorded while he performed left or right hand movement imagination. Different feature sets extracted from EEG were used as inputs into linear, Neural Network and HMM classifiers for the purpose of imagery movement mental task classification. The results indicate that applying linear classifier to 5 frequency features of asymmetry signal produced from channel C3 and C4 can provide a very high classification accuracy percentage as a simple classifier with small number of features comparing to other feature sets.

[BibTex]

[BibTex]


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Building Sparse Large Margin Classifiers

Wu, M., Schölkopf, B., BakIr, G.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 996-1003, (Editors: L De Raedt and S Wrobel ), ACM, New York, NY, USA, ICML , August 2005 (inproceedings)

Abstract
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The added constraint explicitly controls the sparseness of the classifier and an approach is provided to solve the formulated problem. When considering the dual of this problem, it can be seen that building an SLMC is equivalent to constructing an SVM with a modified kernel function. Further analysis of this kernel function indicates that the proposed approach essentially finds a discriminating subspace that can be spanned by a small number of vectors, and in this subspace different classes of data are linearly well separated. Experimental results over several classification benchmarks show that in most cases the proposed approach outperforms the state-of-art sparse learning algorithms.

PDF DOI [BibTex]

PDF DOI [BibTex]