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ORCIDMarius Kloft
Person information
- affiliation: University of Kaiserslautern, Department of Computer Science, Germany
- affiliation: Humboldt University of Berlin, Department of Computer Science, Germany
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2020 – today
- 2022
[i42]Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi-Latif, Steffen Staab, Stephan Mandt, Maja Rudolph:
Detecting Anomalies within Time Series using Local Neural Transformations. CoRR abs/2202.03944 (2022)- [i41]Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt:
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. CoRR abs/2202.08088 (2022) - [i40]Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft:
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images. CoRR abs/2205.11474 (2022) - [i39]Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph:
Raising the Bar in Graph-level Anomaly Detection. CoRR abs/2205.13845 (2022) - 2021
- [j18]Xinwang Liu
, Miaomiao Li, Chang Tang, Jingyuan Xia, Jian Xiong, Li Liu, Marius Kloft, En Zhu:
Efficient and Effective Regularized Incomplete Multi-View Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 43(8): 2634-2646 (2021) - [j17]Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller:
A Unifying Review of Deep and Shallow Anomaly Detection. Proc. IEEE 109(5): 756-795 (2021) - [c52]Lijun Zhou, Antoine Ledent, Qintao Hu, Ting Liu, Jianlin Zhang, Marius Kloft:
Model Uncertainty Guides Visual Object Tracking. AAAI 2021: 3581-3589 - [c51]Antoine Ledent, Waleed Mustafa, Yunwen Lei, Marius Kloft:
Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks. AAAI 2021: 8279-8287 - [c50]Liang Wu, Antoine Ledent, Yunwen Lei, Marius Kloft:
Fine-grained Generalization Analysis of Vector-Valued Learning. AAAI 2021: 10338-10346 - [c49]Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller:
Explainable Deep One-Class Classification. ICLR 2021 - [c48]Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph:
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. ICML 2021: 8703-8714 - [c47]Saurabh Varshneya, Antoine Ledent, Robert A. Vandermeulen, Yunwen Lei, Matthias Enders, Damian Borth, Marius Kloft:
Learning Interpretable Concept Groups in CNNs. IJCAI 2021: 1061-1067 - [c46]Waleed Mustafa, Yunwen Lei, Antoine Ledent, Marius Kloft:
Fine-grained Generalization Analysis of Structured Output Prediction. IJCAI 2021: 2841-2847 - [c45]Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft:
Fine-grained Generalization Analysis of Inductive Matrix Completion. NeurIPS 2021: 25540-25552 - [c44]Rodrigo Alves, Antoine Ledent, Marius Kloft:
Burst-induced Multi-Armed Bandit for Learning Recommendation. RecSys 2021: 292-301 - [i38]Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph:
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. CoRR abs/2103.16440 (2021) - [i37]Liang Wu, Antoine Ledent, Yunwen Lei, Marius Kloft:
Fine-grained Generalization Analysis of Vector-valued Learning. CoRR abs/2104.14173 (2021) - [i36]Waleed Mustafa, Yunwen Lei, Antoine Ledent, Marius Kloft:
Fine-grained Generalization Analysis of Structured Output Prediction. CoRR abs/2106.00115 (2021) - [i35]Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft:
Explaining Bayesian Neural Networks. CoRR abs/2108.10346 (2021) - [i34]Matthias Kirchler, Martin Graf, Marius Kloft, Christoph Lippert:
Explainability Requires Interactivity. CoRR abs/2109.07869 (2021) - [i33]Saurabh Varshneya, Antoine Ledent, Robert A. Vandermeulen, Yunwen Lei, Matthias Enders, Damian Borth, Marius Kloft:
Learning Interpretable Concept Groups in CNNs. CoRR abs/2109.10078 (2021) - [i32]Billy Joe Franks, Markus Anders, Marius Kloft, Pascal Schweitzer:
Trainability for Universal GNNs Through Surgical Randomness. CoRR abs/2112.04314 (2021) - 2020
- [j16]Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Lei Wang, En Zhu, Tongliang Liu, Marius Kloft, Dinggang Shen, Jianping Yin, Wen Gao:
Multiple Kernel $k$k-Means with Incomplete Kernels. IEEE Trans. Pattern Anal. Mach. Intell. 42(5): 1191-1204 (2020) - [c43]Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert:
Two-sample Testing Using Deep Learning. AISTATS 2020: 1387-1398 - [c42]Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft:
Deep Semi-Supervised Anomaly Detection. ICLR 2020 - [c41]Penny Chong, Lukas Ruff, Marius Kloft, Alexander Binder:
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification. IJCNN 2020: 1-9 - [c40]Guang Yu, Siqi Wang, Zhiping Cai, En Zhu, Chuanfu Xu, Jianping Yin, Marius Kloft:
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events. ACM Multimedia 2020: 583-591 - [c39]Yunwen Lei, Antoine Ledent, Marius Kloft:
Sharper Generalization Bounds for Pairwise Learning. NeurIPS 2020 - [c38]Rodrigo Alves, Antoine Ledent, Renato Assunção, Marius Kloft:
An Empirical Study of the Discreteness Prior in Low-Rank Matrix Completion. Preregister@NeurIPS 2020: 111-125 - [i31]Penny Chong, Lukas Ruff, Marius Kloft, Alexander Binder:
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification. CoRR abs/2001.08873 (2020) - [i30]Antoine Ledent, Rodrigo Alves, Marius Kloft:
Orthogonal Inductive Matrix Completion. CoRR abs/2004.01653 (2020) - [i29]Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft:
Rethinking Assumptions in Deep Anomaly Detection. CoRR abs/2006.00339 (2020) - [i28]Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft:
How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. CoRR abs/2006.09000 (2020) - [i27]Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller:
Explainable Deep One-Class Classification. CoRR abs/2007.01760 (2020) - [i26]Guang Yu, Siqi Wang, Zhiping Cai, En Zhu, Chuanfu Xu, Jianping Yin, Marius Kloft:
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events. CoRR abs/2008.11988 (2020) - [i25]Waleed Mustafa, Robert A. Vandermeulen, Marius Kloft:
Input Hessian Regularization of Neural Networks. CoRR abs/2009.06571 (2020) - [i24]Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller:
A Unifying Review of Deep and Shallow Anomaly Detection. CoRR abs/2009.11732 (2020)
2010 – 2019
- 2019
- [j15]Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft:
Data-Dependent Generalization Bounds for Multi-Class Classification. IEEE Trans. Inf. Theory 65(5): 2995-3021 (2019) - [c37]Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper:
Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation. AAAI 2019: 5417-5424 - [c36]Lukas Ruff, Yury Zemlyanskiy, Robert A. Vandermeulen, Thomas Schnake, Marius Kloft:
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text. ACL (1) 2019: 4061-4071 - [c35]Thomas Goerttler, Marius Kloft:
Learning a Multimodal Prior Distribution for Generative Adversarial Nets. LWDA 2019: 94-105 - [c34]Siqi Wang, Yijie Zeng, Xinwang Liu, En Zhu, Jianping Yin, Chuanfu Xu, Marius Kloft:
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network. NeurIPS 2019: 5960-5973 - [i23]Antoine Ledent, Yunwen Lei, Marius Kloft:
Improved Generalisation Bounds for Deep Learning Through L∞ Covering Numbers. CoRR abs/1905.12430 (2019) - [i22]Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft:
Deep Semi-Supervised Anomaly Detection. CoRR abs/1906.02694 (2019) - [i21]James A. Preiss, Sébastien M. R. Arnold, Chen-Yu Wei, Marius Kloft:
Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator. CoRR abs/1910.01249 (2019) - [i20]Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert:
Two-sample Testing Using Deep Learning. CoRR abs/1910.06239 (2019) - 2018
- [j14]Yanhua Chen, Marius Kloft, Yi Yang, Caihong Li, Lian Li:
Mixed kernel based extreme learning machine for electric load forecasting. Neurocomputing 312: 90-106 (2018) - [j13]Niloofar Yousefi, Yunwen Lei, Marius Kloft, Mansooreh Mollaghasemi, Georgios C. Anagnostopoulos:
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning. J. Mach. Learn. Res. 19: 38:1-38:47 (2018) - [j12]Nico Görnitz, Luiz Alberto Lima, Klaus-Robert Müller, Marius Kloft, Shinichi Nakajima:
Support Vector Data Descriptions and k-Means Clustering: One Class? IEEE Trans. Neural Networks Learn. Syst. 29(9): 3994-4006 (2018) - [c33]Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt:
Scalable Generalized Dynamic Topic Models. AISTATS 2018: 1427-1435 - [c32]Lukas Ruff, Nico Görnitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert A. Vandermeulen, Alexander Binder, Emmanuel Müller, Marius Kloft:
Deep One-Class Classification. ICML 2018: 4390-4399 - [c31]Lucas Deecke, Robert A. Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft:
Image Anomaly Detection with Generative Adversarial Networks. ECML/PKDD (1) 2018: 3-17 - [c30]Marius Kloft:
Distributed Optimization of All-in-one SVMs for Extreme Classfication. WWW (Companion Volume) 2018: 1899 - [i19]Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper:
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation. CoRR abs/1802.06383 (2018) - [i18]Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt:
Scalable Generalized Dynamic Topic Models. CoRR abs/1803.07868 (2018) - [i17]Samy Bengio, Krzysztof Dembczynski, Thorsten Joachims, Marius Kloft, Manik Varma:
Extreme Classification (Dagstuhl Seminar 18291). Dagstuhl Reports 8(7): 62-80 (2018) - 2017
- [j11]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse probit linear mixed model. Mach. Learn. 106(9-10): 1621-1642 (2017) - [c29]Florian Wenzel, Théo Galy-Fajou, Matthäus Deutsch, Marius Kloft:
Bayesian Nonlinear Support Vector Machines for Big Data. ECML/PKDD (1) 2017: 307-322 - [i16]Yunwen Lei, Ürün Dogan, Ding-Xuan Zhou, Marius Kloft:
Generalization Error Bounds for Extreme Multi-class Classification. CoRR abs/1706.09814 (2017) - [i15]Florian Wenzel, Théo Galy-Fajou, Matthäus Deutsch, Marius Kloft:
Bayesian Nonlinear Support Vector Machines for Big Data. CoRR abs/1707.05532 (2017) - 2016
- [c28]Yunwen Lei, Alexander Binder, Ürün Dogan, Marius Kloft:
Localized Multiple Kernel Learning - A Convex Approach. ACML 2016: 81-96 - [c27]Matthias Kirchler, Dominik Herrmann, Jens Lindemann, Marius Kloft:
Tracked Without a Trace: Linking Sessions of Users by Unsupervised Learning of Patterns in Their DNS Traffic. AISec@CCS 2016: 23-34 - [c26]Oleksandr Zadorozhnyi, Gunthard Benecke, Stephan Mandt, Tobias Scheffer, Marius Kloft:
Huber-Norm Regularization for Linear Prediction Models. ECML/PKDD (1) 2016: 714-730 - [c25]Dominik Herrmann, Matthias Kirchler, Jens Lindemann, Marius Kloft:
Behavior-based tracking of Internet users with semi-supervised learning. PST 2016: 596-599 - [c24]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft:
Separating Sparse Signals from Correlated Noise in Binary Classification. CFA@UAI 2016: 48-58 - [i14]Niloofar Yousefi, Yunwen Lei, Marius Kloft, Mansooreh Mollaghasemi, Georgios C. Anagnostopoulos:
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning. CoRR abs/1602.05916 (2016) - [i13]Marina M.-C. Vidovic, Nico Görnitz, Klaus-Robert Müller, Marius Kloft:
Feature Importance Measure for Non-linear Learning Algorithms. CoRR abs/1611.07567 (2016) - [i12]Maximilian Alber, Julian Zimmert, Ürün Dogan, Marius Kloft:
Distributed Optimization of Multi-Class SVMs. CoRR abs/1611.08480 (2016) - 2015
- [j10]Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch:
Probabilistic clustering of time-evolving distance data. Mach. Learn. 100(2-3): 635-654 (2015) - [j9]Anne K. Porbadnigk, Nico Görnitz, Claudia Sannelli, Alexander Binder, Mikio L. Braun, Marius Kloft, Klaus-Robert Müller:
Extracting latent brain states - Towards true labels in cognitive neuroscience experiments. NeuroImage 120: 225-253 (2015) - [c23]Nico Görnitz, Mikio L. Braun, Marius Kloft:
Hidden Markov Anomaly Detection. ICML 2015: 1833-1842 - [c22]Yunwen Lei, Alexander Binder, Ürün Dogan, Marius Kloft:
Theory and Algorithms for the Localized Setting of Learning Kernels. FE@NIPS 2015: 173-195 - [c21]Yunwen Lei, Ürün Dogan, Alexander Binder, Marius Kloft:
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms. NIPS 2015: 2035-2043 - [c20]Marina M.-C. Vidovic, Nico Görnitz, Klaus-Robert Müller, Gunnar Rätsch, Marius Kloft:
Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms. ECML/PKDD (2) 2015: 137-153 - [i11]Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch:
Probabilistic Clustering of Time-Evolving Distance Data. CoRR abs/1504.03701 (2015) - [i10]Yunwen Lei, Ürün Dogan, Alexander Binder, Marius Kloft:
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms. CoRR abs/1506.04359 (2015) - [i9]Yunwen Lei, Alexander Binder, Ürün Dogan, Marius Kloft:
Localized Multiple Kernel Learning - A Convex Approach. CoRR abs/1506.04364 (2015) - [i8]Christian Widmer, Marius Kloft, Vipin T. Sreedharan, Gunnar Rätsch:
Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis. CoRR abs/1506.09153 (2015) - [i7]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse Estimation in a Correlated Probit Model. CoRR abs/1507.04777 (2015) - [i6]Trevor Darrell, Marius Kloft, Massimiliano Pontil, Gunnar Rätsch, Erik Rodner:
Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). Dagstuhl Reports 5(4): 18-55 (2015) - 2014
- [j8]Christian Widmer, Marius Kloft, Xinghua Lou, Gunnar Rätsch:
Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging. Künstliche Intell. 28(1): 29-33 (2014) - [j7]Alexander Bauer, Nico Görnitz, Franziska Biegler, Klaus-Robert Müller, Marius Kloft:
Efficient Algorithms for Exact Inference in Sequence Labeling SVMs. IEEE Trans. Neural Networks Learn. Syst. 25(5): 870-881 (2014) - [c19]Nico Görnitz, Anne Porbadnigk, Alexander Binder, Claudia Sannelli, Mikio L. Braun, Klaus-Robert Müller, Marius Kloft:
Learning and Evaluation in Presence of Non-i.i.d. Label Noise. AISTATS 2014: 293-302 - [c18]Anne K. Porbadnigk, Nico Görnitz, Claudia Sannelli, Alexander Binder, Mikio L. Braun, Marius Kloft, Klaus-Robert Müller:
When brain and behavior disagree: Tackling systematic label noise in EEG data with machine learning. BCI 2014: 1-4 - [c17]Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft:
Localized Complexities for Transductive Learning. COLT 2014: 857-884 - [i5]Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld:
Toward Supervised Anomaly Detection. CoRR abs/1401.6424 (2014) - [i4]Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft:
Localized Complexities for Transductive Learning. CoRR abs/1411.7200 (2014) - 2013
- [j6]Marius Kloft:
Kernel-Based Machine Learning with Multiple Sources of Information. it Inf. Technol. 55(2): 76- (2013) - [j5]Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld:
Toward Supervised Anomaly Detection. J. Artif. Intell. Res. 46: 235-262 (2013) - [j4]Anne Porbadnigk, Nico Görnitz, Marius Kloft, Klaus-Robert Müller:
Decoding Brain States during Auditory Perception by Supervising Unsupervised Learning. J. Comput. Sci. Eng. 7(2): 112-121 (2013) - [c16]Christian Widmer, Marius Kloft, Gunnar Rätsch:
Multi-task Learning for Computational Biology: Overview and Outlook. Empirical Inference 2013: 117-127 - [c15]Corinna Cortes, Marius Kloft, Mehryar Mohri:
Learning Kernels Using Local Rademacher Complexity. NIPS 2013: 2760-2768 - 2012
- [j3]Marius Kloft, Gilles Blanchard:
On the convergence rate of lp-norm multiple kernel learning. J. Mach. Learn. Res. 13: 2465-2502 (2012) - [j2]Marius Kloft, Pavel Laskov:
Security analysis of online centroid anomaly detection. J. Mach. Learn. Res. 13: 3681-3724 (2012) - [c14]Kristof Schütt, Marius Kloft, Alexander Bikadorov, Konrad Rieck:
Early detection of malicious behavior in JavaScript code. AISec 2012: 15-24 - [c13]Christian Widmer, Marius Kloft, Nico Görnitz, Gunnar Rätsch:
Efficient Training of Graph-Regularized Multitask SVMs. ECML/PKDD (1) 2012: 633-647 - 2011
- [b1]Marius Kloft:
lp-Norm Multiple Kernel Learning. Berlin Institute of Technology, 2011 - [j1]Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien:
lp-Norm Multiple Kernel Learning. J. Mach. Learn. Res. 12: 953-997 (2011) - [c12]Alexander Binder, Wojciech Samek, Marius Kloft, Christina Müller, Klaus-Robert Müller, Motoaki Kawanabe:
The Joint Submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task. CLEF (Notebook Papers/Labs/Workshop) 2011 - [c11]Robert Jenssen, Marius Kloft, Sören Sonnenburg, Alexander Zien, Klaus-Robert Müller:
A new scatter-based multi-class support vector machine. MLSP 2011: 1-6 - [c10]Marius Kloft, Gilles Blanchard:
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning. NIPS 2011: 2438-2446 - [c9]Ulrich Rückert, Marius Kloft:
Transfer Learning with Adaptive Regularizers. ECML/PKDD (3) 2011: 65-80 - [p1]Marius Kloft:
Maschinelles Lernen mit multiplen Kernen. Ausgezeichnete Informatikdissertationen 2011: 111-120 - [i3]Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller, Motoaki Kawanabe:
Insights from Classifying Visual Concepts with Multiple Kernel Learning. CoRR abs/1112.3697 (2011) - 2010
- [c8]Marius Kloft, Ulrich Rückert, Peter L. Bartlett:
A Unifying View of Multiple Kernel Learning. ECML/PKDD (2) 2010: 66-81 - [c7]Marius Kloft, Pavel Laskov:
Online Anomaly Detection under Adversarial Impact. AISTATS 2010: 405-412 - [i2]Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien:
Non-Sparse Regularization and Efficient Training with Multiple Kernels. CoRR abs/1003.0079 (2010) - [i1]Marius Kloft, Ulrich Rückert, Peter L. Bartlett:
A Unifying View of Multiple Kernel Learning. CoRR abs/1005.0437 (2010)
2000 – 2009
- 2009
- [c6]Pavel Laskov, Marius Kloft:
A framework for quantitative security analysis of machine learning. AISec 2009: 1-4 - [c5]Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf Brefeld:
Active learning for network intrusion detection. AISec 2009: 47-54 - [c4]Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Pavel Laskov, Klaus-Robert Müller, Alexander Zien:
Efficient and Accurate Lp-Norm Multiple Kernel Learning. NIPS 2009: 997-1005 - [c3]Nico Görnitz, Marius Kloft, Ulf Brefeld:
Active and Semi-supervised Data Domain Description. ECML/PKDD (1) 2009: 407-422 - [c2]Marius Kloft, Shinichi Nakajima, Ulf Brefeld:
Feature Selection for Density Level-Sets. ECML/PKDD (1) 2009: 692-704 - 2008
- [c1]Marius Kloft, Ulf Brefeld, Patrick Düssel, Christian Gehl, Pavel Laskov:
Automatic feature selection for anomaly detection. AISec 2008: 71-76
Coauthor Index