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521 results on '"Brefeld, Ulf"'

3. Joint Optimization of an Autoencoder for Clustering and Embedding

Author

Boubekki, Ahcène, Kampffmeyer, Michael, Jenssen, Robert, and Brefeld, Ulf

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternates between the training of a deep autoencoder and a k-means clustering of the autoencoder's embedding. The diachronic setting, however, prevents the former to benefit from valuable information acquired by the latter. In this paper, we present an alternative where the autoencoder and the clustering are learned simultaneously. This is achieved by providing novel theoretical insight, where we show that the objective function of a certain class of Gaussian mixture models (GMMs) can naturally be rephrased as the loss function of a one-hidden layer autoencoder thus inheriting the built-in clustering capabilities of the GMM. That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model able to jointly learn a clustering and an embedding. Experiments confirm the equivalence between the clustering module and Gaussian mixture models. Further evaluations affirm the empirical relevance of our deep architecture as it outperforms related baselines on several data sets.

Published
2020

5. User Authentication via Multifaceted Mouse Movements and Outlier Exposure

Author

Matthiesen, Jennifer J., Hastedt, Hanne, Brefeld, Ulf, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crémilleux, Bruno, editor, Hess, Sibylle, editor, and Nijssen, Siegfried, editor

Published
2023
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8. Principled Interpolation in Normalizing Flows

Author

Fadel, Samuel G., Mair, Sebastian, Torres, Ricardo da S., and Brefeld, Ulf

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Generative models based on normalizing flows are very successful in modeling complex data distributions using simpler ones. However, straightforward linear interpolations show unexpected side effects, as interpolation paths lie outside the area where samples are observed. This is caused by the standard choice of Gaussian base distributions and can be seen in the norms of the interpolated samples. This observation suggests that correcting the norm should generally result in better interpolations, but it is not clear how to correct the norm in an unambiguous way. In this paper, we solve this issue by enforcing a fixed norm and, hence, change the base distribution, to allow for a principled way of interpolation. Specifically, we use the Dirichlet and von Mises-Fisher base distributions. Our experimental results show superior performance in terms of bits per dimension, Fr\'echet Inception Distance (FID), and Kernel Inception Distance (KID) scores for interpolation, while maintaining the same generative performance., Comment: Under review

Published
2020
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9. Evaluating one-shot tournament predictions

Author

Ekstrøm, Claus Thorn, Van Eetvelde, Hans, Ley, Christophe, and Brefeld, Ulf

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

We introduce the Tournament Rank Probability Score (TRPS) as a measure to evaluate and compare pre-tournament predictions, where predictions of the full tournament results are required to be available before the tournament begins. The TRPS handles partial ranking of teams, gives credit to predictions that are only slightly wrong, and can be modified with weights to stress the importance of particular features of the tournament prediction. Thus, the Tournament Rank Prediction Score is more flexible than the commonly preferred log loss score for such tasks. In addition, we show how predictions from historic tournaments can be optimally combined into ensemble predictions in order to maximize the TRPS for a new tournament., Comment: 11 pages, 2 figures

Published
2019

10. Mining User Trajectories in Electronic Text Books

Author

Boubekki, Ahcène, Jain, Shailee, and Brefeld, Ulf

Abstract

Analyzing user behavior in electronic textbooks offers appealing insights into how pupils interact with the book and internalize the content. Using these insights may help to personalize the book, e.g., to support users with special educational needs. Conventional approaches often focus on atomic, user-triggered events like clicks or scrolls. In this paper, we propose to view all ongoing sessions in a classroom simultaneously and cast the problem as a multi-user problem over space and time. We devise two distance measures to compare the navigation behavior of pupils in different dimensions. Empirically, we observe that our metrics lead to interpretable clusters and serve as performance indicators. [For the full proceedings, see ED593090.]

Published
2018

12. Simultaneous Constrained Adaptive Item Selection for Group-Based Testing

Author

Bengs, Daniel, Kroehne, Ulf, and Brefeld, Ulf

Abstract

By tailoring test forms to the test-taker's proficiency, Computerized Adaptive Testing (CAT) enables substantial increases in testing efficiency over fixed forms testing. When used for formative assessment, the alignment of task difficulty with proficiency increases the chance that teachers can derive useful feedback from assessment data. The application of CAT to formative assessment in the classroom, however, is hindered by the large number of different items used for the whole class; the required familiarization with a large number of test items puts a significant burden on teachers. An improved CAT procedure for group-based testing is presented, which uses simultaneous automated test assembly to impose a limit on the number of items used per group. The proposed linear model for simultaneous adaptive item selection allows for full adaptivity and the accommodation of constraints on test content. The effectiveness of the group-based CAT is demonstrated with real-world items in a simulated adaptive test of 3,000 groups of test-takers, under different assumptions on group composition. Results show that the group-based CAT maintained the efficiency of CAT, while a reduction in the number of used items by one half to two-thirds was achieved, depending on the within-group variance of proficiencies.

Published
2021
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13. Soccer Analytics – Analysing Tracking Data

Author

Brefeld, Ulf, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Baca, Arnold, editor, and Exel, Juliana, editor

Published
2023
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15. Infinite Mixture Model of Markov Chains

Author

Reubold, Jan, Strufe, Thorsten, and Brefeld, Ulf

Subjects
Statistics - Machine Learning
Abstract

We propose a Bayesian nonparametric mixture model for prediction- and information extraction tasks with an efficient inference scheme. It models categorical-valued time series that exhibit dynamics from multiple underlying patterns (e.g. user behavior traces). We simplify the idea of capturing these patterns by hierarchical hidden Markov models (HHMMs) - and extend the existing approaches by the additional representation of structural information. Our empirical results are based on both synthetic- and real world data. They indicate that the results are easily interpretable, and that the model excels at segmentation and prediction performance: it successfully identifies the generating patterns and can be used for effective prediction of future observations.

Published
2017

16. Principled Interpolation in Normalizing Flows

Author

Fadel, Samuel G., Mair, Sebastian, da S. Torres, Ricardo, Brefeld, Ulf, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Oliver, Nuria, editor, Pérez-Cruz, Fernando, editor, Kramer, Stefan, editor, Read, Jesse, editor, and Lozano, Jose A., editor

Published
2021
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17. Assessing User Behavior by Mouse Movements

Author

Matthiesen, Jennifer Jorina, Brefeld, Ulf, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Stephanidis, Constantine, editor, and Antona, Margherita, editor

Published
2020
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19. Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring

Author

Pandarova, Irina, Schmidt, Torben, Hartig, Johannes, Boubekki, Ahcène, Jones, Roger Dale, and Brefeld, Ulf

Abstract

Advances in computer technology and artificial intelligence create opportunities for developing adaptive language learning technologies which are sensitive to individual learner characteristics. This paper focuses on one form of adaptivity in which the difficulty of learning content is dynamically adjusted to the learner's evolving language ability. A pilot study is presented which aims to advance the (semi-)automatic difficulty scoring of grammar exercise items to be used in dynamic difficulty adaptation in an intelligent language tutoring system for practicing English tenses. In it, methods from item response theory and machine learning are combined with linguistic item analysis in order to calibrate the difficulty of an initial exercise pool of cued gap-filling items (CGFIs) and isolate CGFI features predictive of item difficulty. Multiple item features at the gap, context and CGFI levels are tested and relevant predictors are identified at all three levels. Our pilot regression models reach encouraging prediction accuracy levels which could, pending additional validation, enable the dynamic selection of newly generated items ranging from moderately easy to moderately difficult. The paper highlights further applications of the proposed methodology in the area of adapting language tutoring, item design and second language acquisition, and sketches out issues for future research.

Published
2019
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20. Personalized Transaction Kernels for Recommendation Using MCTS

Author

Tavakol, Maryam, Joppen, Tobias, Brefeld, Ulf, Fürnkranz, Johannes, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Benzmüller, Christoph, editor, and Stuckenschmidt, Heiner, editor

Published
2019
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21. Frame-Based Optimal Design

Author

Mair, Sebastian, Rudolph, Yannick, Closius, Vanessa, Brefeld, Ulf, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Berlingerio, Michele, editor, Bonchi, Francesco, editor, Gärtner, Thomas, editor, Hurley, Neil, editor, and Ifrim, Georgiana, editor

Published
2019
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22. Toward Supervised Anomaly Detection

Author

Goernitz, Nico, Kloft, Marius Micha, Rieck, Konrad, and Brefeld, Ulf

Subjects
Computer Science - Learning
Abstract

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to ground on the unsupervised learning paradigm and devise a novel algorithm that meets this requirement. Although being intrinsically non-convex, we further show that the optimization problem has a convex equivalent under relatively mild assumptions. Additionally, we propose an active learning strategy to automatically filter candidates for labeling. In an empirical study on network intrusion detection data, we observe that the proposed learning methodology requires much less labeled data than the state-of-the-art, while achieving higher detection accuracies.

Published
2014
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23. Infinite Mixtures of Markov Chains

Author

Reubold, Jan, Boubekki, Ahcène, Strufe, Thorsten, Brefeld, Ulf, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Appice, Annalisa, editor, Loglisci, Corrado, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, and Ras, Zbigniew W., editor

Published
2018
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24. MDP-based Itinerary Recommendation using Geo-Tagged Social Media

Author

Gaonkar, Radhika, Tavakol, Maryam, Brefeld, Ulf, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Duivesteijn, Wouter, editor, Siebes, Arno, editor, and Ukkonen, Antti, editor

Published
2018
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26. Insights from Classifying Visual Concepts with Multiple Kernel Learning

Author

Binder, Alexander, Nakajima, Shinichi, Kloft, Marius, Müller, Christina, Samek, Wojciech, Brefeld, Ulf, Müller, Klaus-Robert, and Kawanabe, Motoaki

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, so-called 1-norm MKL variants are often observed to be outperformed by an unweighted sum kernel. The contribution of this paper is twofold: We apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks within computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum kernel SVM and the sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. About to be submitted to PLoS ONE., Comment: 18 pages, 8 tables, 4 figures, format deviating from plos one submission format requirements for aesthetic reasons

Published
2011
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27. Non-Sparse Regularization for Multiple Kernel Learning

Author

Kloft, Marius, Brefeld, Ulf, Sonnenburg, Soeren, and Zien, Alexander

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability and scalability. Unfortunately, this 1-norm MKL is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures, we generalize MKL to arbitrary norms. We devise new insights on the connection between several existing MKL formulations and develop two efficient interleaved optimization strategies for arbitrary norms, like p-norms with p>1. Empirically, we demonstrate that the interleaved optimization strategies are much faster compared to the commonly used wrapper approaches. A theoretical analysis and an experiment on controlled artificial data experiment sheds light on the appropriateness of sparse, non-sparse and $\ell_\infty$-norm MKL in various scenarios. Empirical applications of p-norm MKL to three real-world problems from computational biology show that non-sparse MKL achieves accuracies that go beyond the state-of-the-art.

Published
2010

29. Propagating Maximum Capacities for Recommendation

Author

Boubekki, Ahcène, Brefeld, Ulf, Lucchesi, Cláudio Leonardo, Stille, Wolfgang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kern-Isberner, Gabriele, editor, Fürnkranz, Johannes, editor, and Thimm, Matthias, editor

Published
2017
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31. Data-Driven Analyses of Electronic Text Books

Author

Boubekki, Ahcène, Kröhne, Ulf, Goldhammer, Frank, Schreiber, Waltraud, Brefeld, Ulf, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Michaelis, Stefan, editor, Piatkowski, Nico, editor, and Stolpe, Marco, editor

Published
2016
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32. Using Wikipedia for Cross-Language Named Entity Recognition

Author

Fernandes, Eraldo R., Brefeld, Ulf, Blanco, Roi, Atserias, Jordi, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Atzmueller, Martin, editor, Chin, Alvin, editor, Janssen, Frederik, editor, Schweizer, Immanuel, editor, and Trattner, Christoph, editor

Published
2016
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35. Preface

Author

Brefeld, Ulf, Davis, Jesse, Van Haaren, Jan, Zimmermann, Albrecht, Brefeld, Ulf, Davis, Jesse, Van Haaren, Jan, and Zimmermann, Albrecht

Subjects
Informatics, Business informatics
Published
2023

36. Mining Positional Data Streams

Author

Haase, Jens, Brefeld, Ulf, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Appice, Annalisa, editor, Ceci, Michelangelo, editor, Loglisci, Corrado, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, and Ras, Zbigniew W., editor

Published
2015
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41. Learning from Partially Annotated Sequences

Author

Fernandes, Eraldo R., Brefeld, Ulf, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Gunopulos, Dimitrios, editor, Hofmann, Thomas, editor, Malerba, Donato, editor, and Vazirgiannis, Michalis, editor

Published
2011
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42. Efficient Classification of Images with Taxonomies

Author

Binder, Alexander, Kawanabe, Motoaki, Brefeld, Ulf, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zha, Hongbin, editor, Taniguchi, Rin-ichiro, editor, and Maybank, Stephen, editor

Published
2010
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43. Feature Selection for Density Level-Sets

Author

Kloft, Marius, Nakajima, Shinichi, Brefeld, Ulf, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Buntine, Wray, editor, Grobelnik, Marko, editor, Mladenić, Dunja, editor, and Shawe-Taylor, John, editor

Published
2009
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44. Active and Semi-supervised Data Domain Description

Author

Görnitz, Nico, Kloft, Marius, Brefeld, Ulf, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Buntine, Wray, editor, Grobelnik, Marko, editor, Mladenić, Dunja, editor, and Shawe-Taylor, John, editor

Published
2009
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45. Preface

Author

Brefeld, Ulf, Davis, Jesse, Haaren, Jan Van, Zimmermann, Albrecht, Brefeld, Ulf, Davis, Jesse, Haaren, Jan van, and Zimmermann, Albrecht

Subjects
Informatics
Published
2022

50. Multi-view Discriminative Sequential Learning

Author

Brefeld, Ulf, Büscher, Christoph, Scheffer, Tobias, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gama, João, editor, Camacho, Rui, editor, Brazdil, Pavel B., editor, Jorge, Alípio Mário, editor, and Torgo, Luís, editor

Published
2005
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