Header logo is


2020


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]

2020


arXiv [BibTex]

2019


no image
Selecting causal brain features with a single conditional independence test per feature

Mastakouri, A., Schölkopf, B., Janzing, D.

Advances in Neural Information Processing Systems 32, pages: 12532-12543, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

2019


link (url) [BibTex]


no image
Practical and Consistent Estimation of f-Divergences

Rubenstein, P. K., Bousquet, O., Djolonga, J., Riquelme, C., Tolstikhin, I.

Advances in Neural Information Processing Systems 32, pages: 4072-4082, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Invert to Learn to Invert

Putzky, P., M., W.

Advances in Neural Information Processing Systems 32, pages: 444-454, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Limitations of the empirical Fisher approximation for natural gradient descent

Kunstner, F., Hennig, P., Balles, L.

Advances in Neural Information Processing Systems 32, pages: 4158-4169, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei pn

link (url) [BibTex]

link (url) [BibTex]


no image
On the Fairness of Disentangled Representations

Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B., Bachem, O.

Advances in Neural Information Processing Systems 32, pages: 14584-14597, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
A Model to Search for Synthesizable Molecules

Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., Hernández-Lobato, J. M.

Advances in Neural Information Processing Systems 32, pages: 7935-7947, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Gondal, M. W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., Bauer, S.

Advances in Neural Information Processing Systems 32, pages: 15714-15725, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei sf am

link (url) [BibTex]

link (url) [BibTex]


no image
Kernel Stein Tests for Multiple Model Comparison

Lim, J. N., Yamada, M., Schölkopf, B., Jitkrittum, W.

Advances in Neural Information Processing Systems 32, pages: 2240-2250, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Convergence Guarantees for Adaptive Bayesian Quadrature Methods

Kanagawa, M., Hennig, P.

Advances in Neural Information Processing Systems 32, pages: 6234-6245, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei pn

link (url) [BibTex]

link (url) [BibTex]


no image
Are Disentangled Representations Helpful for Abstract Visual Reasoning?

van Steenkiste, S., Locatello, F., Schmidhuber, J., Bachem, O.

Advances in Neural Information Processing Systems 32, pages: 14222-14235, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Perceiving the arrow of time in autoregressive motion

Meding, K., Janzing, D., Schölkopf, B., Wichmann, F. A.

Advances in Neural Information Processing Systems 32, pages: 2303-2314, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Fisher Efficient Inference of Intractable Models

Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y.

Advances in Neural Information Processing Systems 32, pages: 8790-8800, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


no image
Stochastic Frank-Wolfe for Composite Convex Minimization

Locatello, F., Yurtsever, A., Fercoq, O., Cevher, V.

Advances in Neural Information Processing Systems 32, pages: 14246-14256, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Neural Signatures of Motor Skill in the Resting Brain

Ozdenizci, O., Meyer, T., Wichmann, F., Peters, J., Schölkopf, B., Cetin, M., Grosse-Wentrup, M.

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), October 2019 (conference) Accepted

ei

[BibTex]

[BibTex]


no image
Beta Power May Mediate the Effect of Gamma-TACS on Motor Performance

Mastakouri, A., Schölkopf, B., Grosse-Wentrup, M.

Engineering in Medicine and Biology Conference (EMBC), July 2019 (conference) Accepted

ei

arXiv PDF [BibTex]

arXiv PDF [BibTex]


no image
Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 49, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., Silva, R.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 213, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA

Gresele*, L., Rubenstein*, P. K., Mehrjou, A., Locatello, F., Schölkopf, B.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 53, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., Kersting, K., Ghahramani, Z.

Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), pages: 124, (Editors: Amir Globerson and Ricardo Silva), AUAI Press, July 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Kernel Mean Matching for Content Addressability of GANs

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 3140-3151, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019, *equal contribution (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

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

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 4114-4124, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


Local Temporal Bilinear Pooling for Fine-grained Action Parsing
Local Temporal Bilinear Pooling for Fine-grained Action Parsing

Zhang, Y., Tang, S., Muandet, K., Jarvers, C., Neumann, H.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, June 2019 (inproceedings)

Abstract
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.

ei ps

Code video demo pdf link (url) [BibTex]

Code video demo pdf link (url) [BibTex]


no image
Generate Semantically Similar Images with Kernel Mean Matching

Jitkrittum*, W., Sangkloy*, P., Gondal, M. W., Raj, A., Hays, J., Schölkopf, B.

6th Workshop Women in Computer Vision (WiCV) (oral presentation), June 2019, *equal contribution (conference) Accepted

ei

[BibTex]

[BibTex]


no image
Projections for Approximate Policy Iteration Algorithms

Akrour, R., Pajarinen, J., Peters, J., Neumann, G.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 181-190, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Switching Linear Dynamics for Variational Bayes Filtering

Becker-Ehmck, P., Peters, J., van der Smagt, P.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 553-562, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

Suter, R., Miladinovic, D., Schölkopf, B., Bauer, S.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 6056-6065, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

Simon-Gabriel, C., Ollivier, Y., Bottou, L., Schölkopf, B., Lopez-Paz, D.

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 5809-5817, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Ialongo, A. D., Van Der Wilk, M., Hensman, J., Rasmussen, C. E.

In Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 2931-2940, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (inproceedings)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
Meta learning variational inference for prediction

Gordon, J., Bronskill, J., Bauer, M., Nowozin, S., Turner, R.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


no image
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Lutter, M., Ritter, C., Peters, J.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
DeepOBS: A Deep Learning Optimizer Benchmark Suite

Schneider, F., Balles, L., Hennig, P.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei pn

link (url) [BibTex]

link (url) [BibTex]


no image
Disentangled State Space Models: Unsupervised Learning of Dynamics across Heterogeneous Environments

Miladinović*, D., Gondal*, M. W., Schölkopf, B., Buhmann, J. M., Bauer, S.

Deep Generative Models for Highly Structured Data Workshop at ICLR, May 2019, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., Rätsch, G.

7th International Conference on Learning Representations (ICLR), May 2019 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


no image
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features

von Kügelgen, J., Mey, A., Loog, M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1361-1369, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
Resampled Priors for Variational Autoencoders

Bauer, M., Mnih, A.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 66-75, Proceedings of Machine Learning Research, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


no image
Sobolev Descent

Mroueh, Y., Sercu, T., Raj, A.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 2976-2985, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
Fast and Robust Shortest Paths on Manifolds Learned from Data

Arvanitidis, G., Hauberg, S., Hennig, P., Schober, M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1506-1515, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei pn

PDF link (url) [BibTex]

PDF link (url) [BibTex]


Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

de Roos, F., Hennig, P.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1448-1457, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

Abstract
Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not known a priori, iterative linear algebra methods offer one way to efficiently construct them. For the stochastic optimization problems that dominate contemporary machine learning, however, this approach is not readily available. We propose an iterative algorithm inspired by classic iterative linear solvers that uses a probabilistic model to actively infer a pre-conditioner in situations where Hessian-projections can only be constructed with strong Gaussian noise. The algorithm is empirically demonstrated to efficiently construct effective pre-conditioners for stochastic gradient descent and its variants. Experiments on problems of comparably low dimensionality show improved convergence. In very high-dimensional problems, such as those encountered in deep learning, the pre-conditioner effectively becomes an automatic learning-rate adaptation scheme, which we also empirically show to work well.

pn ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs

Wenk, P., Gotovos, A., Bauer, S., Gorbach, N., Krause, A., Buhmann, J. M.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1351-1360, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (conference)

ei

PDF PDF link (url) [BibTex]

PDF PDF link (url) [BibTex]


no image
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

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

Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, 2019, *equal contribution (conference)

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
MYND: A Platform for Large-scale Neuroscientific Studies

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

Proceedings of the 2019 Conference on Human Factors in Computing Systems (CHI), 2019 (conference) Accepted

ei

[BibTex]

[BibTex]


no image
A Kernel Stein Test for Comparing Latent Variable Models

Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., Gretton, A.

2019 (conference) Submitted

ei

arXiv [BibTex]

arXiv [BibTex]


From Variational to Deterministic Autoencoders
From Variational to Deterministic Autoencoders

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

2019, *equal contribution (conference) Submitted

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]

2009


no image
Learning an Interactive Segmentation System

Nickisch, H., Kohli, P., Rother, C.

Max Planck Institute for Biological Cybernetics, December 2009 (techreport)

Abstract
Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user - a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.

ei

Web [BibTex]

2009


Web [BibTex]


no image
A computational model of human table tennis for robot application

Mülling, K., Peters, J.

In AMS 2009, pages: 57-64, (Editors: Dillmann, R. , J. Beyerer, C. Stiller, M. Zöllner, T. Gindele), Springer, Berlin, Germany, Autonome Mobile Systeme, December 2009 (inproceedings)

Abstract
Table tennis is a difficult motor skill which requires all basic components of a general motor skill learning system. In order to get a step closer to such a generic approach to the automatic acquisition and refinement of table tennis, we study table tennis from a human motor control point of view. We make use of the basic models of discrete human movement phases, virtual hitting points, and the operational timing hypothesis. Using these components, we create a computational model which is aimed at reproducing human-like behavior. We verify the functionality of this model in a physically realistic simulation of a BarrettWAM.

ei

Web DOI [BibTex]

Web DOI [BibTex]


no image
A PAC-Bayesian Approach to Formulation of Clustering Objectives

Seldin, Y., Tishby, N.

In Proceedings of the NIPS 2009 Workshop "Clustering: Science or Art? Towards Principled Approaches", pages: 1-4, NIPS Workshop "Clustering: Science or Art? Towards Principled Approaches", December 2009 (inproceedings)

Abstract
Clustering is a widely used tool for exploratory data analysis. However, the theoretical understanding of clustering is very limited. We still do not have a well-founded answer to the seemingly simple question of “how many clusters are present in the data?”, and furthermore a formal comparison of clusterings based on different optimization objectives is far beyond our abilities. The lack of good theoretical support gives rise to multiple heuristics that confuse the practitioners and stall development of the field. We suggest that the ill-posed nature of clustering problems is caused by the fact that clustering is often taken out of its subsequent application context. We argue that one does not cluster the data just for the sake of clustering it, but rather to facilitate the solution of some higher level task. By evaluation of the clustering’s contribution to the solution of the higher level task it is possible to compare different clusterings, even those obtained by different optimization objectives. In the preceding work it was shown that such an approach can be applied to evaluation and design of co-clustering solutions. Here we suggest that this approach can be extended to other settings, where clustering is applied.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Notes on Graph Cuts with Submodular Edge Weights

Jegelka, S., Bilmes, J.

In pages: 1-6, NIPS Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity & Polyhedra (DISCML), December 2009 (inproceedings)

Abstract
Generalizing the cost in the standard min-cut problem to a submodular cost function immediately makes the problem harder. Not only do we prove NP hardness even for nonnegative submodular costs, but also show a lower bound of (|V |1/3) on the approximation factor for the (s, t) cut version of the problem. On the positive side, we propose and compare three approximation algorithms with an overall approximation factor of O(min{|V |,p|E| log |V |}) that appear to do well in practice.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Learning new basic Movements for Robotics

Kober, J., Peters, J.

In AMS 2009, pages: 105-112, (Editors: Dillmann, R. , J. Beyerer, C. Stiller, M. Zöllner, T. Gindele), Springer, Berlin, Germany, Autonome Mobile Systeme, December 2009 (inproceedings)

Abstract
Obtaining novel skills is one of the most important problems in robotics. Machine learning techniques may be a promising approach for automatic and autonomous acquisition of movement policies. However, this requires both an appropriate policy representation and suitable learning algorithms. Employing the most recent form of the dynamical systems motor primitives originally introduced by Ijspeert et al. [1], we show how both discrete and rhythmic tasks can be learned using a concerted approach of both imitation and reinforcement learning, and present our current best performing learning algorithms. Finally, we show that it is possible to include a start-up phase in rhythmic primitives. We apply our approach to two elementary movements, i.e., Ball-in-a-Cup and Ball-Paddling, which can be learned on a real Barrett WAM robot arm at a pace similar to human learning.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]