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Marius Kloft
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- 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
- 2024
- [j26]Ramon Lopes
, Rodrigo Alves
Recommendations with minimum exposure guarantees: A post-processing framework. Expert Syst. Appl. 236: 121164 (2024) - [c66]Charu James, Mayank Nagda, Nooshin Haji Ghassemi, Marius Kloft, Sophie Fellenz:
Evaluating Dynamic Topic Models. ACL (1) 2024: 160-176 - [c65]Waleed Mustafa, Philipp Liznerski, Antoine Ledent, Dennis Wagner, Puyu Wang, Marius Kloft:
Non-vacuous Generalization Bounds for Adversarial Risk in Stochastic Neural Networks. AISTATS 2024: 4528-4536 - [c64]Phil Sidney Ostheimer, Mayank Kumar Nagda, Marius Kloft, Sophie Fellenz:
Text Style Transfer Evaluation Using Large Language Models. LREC/COLING 2024: 15802-15822 - [c63]Rodrigo Alves
Unraveling the Dynamics of Stable and Curious Audiences in Web Systems. WWW 2024: 2464-2475 - [i59]Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Sophie Fellenz, Marius Kloft:
Reimagining Anomalies: What If Anomalies Were Normal? CoRR abs/2402.14469 (2024) - [i58]Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i57]Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft:
Interpretable Tensor Fusion. CoRR abs/2405.04671 (2024) - [i56]Jonas Dippel, Niklas Prenißl, Julius Hense, Philipp Liznerski, Tobias Winterhoff, Simon Schallenberg, Marius Kloft, Oliver Buchstab, David Horst, Maximilian Alber, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen:
AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics. CoRR abs/2406.14866 (2024) - [i55]Aodong Li, Yunhan Zhao, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Anomaly Detection of Tabular Data Using LLMs. CoRR abs/2406.16308 (2024) - [i54]Florian Dietz, Sophie Fellenz, Dietrich Klakow, Marius Kloft:
Comgra: A Tool for Analyzing and Debugging Neural Networks. CoRR abs/2407.21656 (2024) - 2023
- [j25]Siqi Wang
E$^{3}$3Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 2952-2969 (2023) - [j24]Billy Joe Franks, Markus Anders, Marius Kloft, Pascal Schweitzer:
A Systematic Approach to Universal Random Features in Graph Neural Networks. Trans. Mach. Learn. Res. 2023 (2023) - [j23]Dennis Wagner, Tobias Michels, Florian C. F. Schulz, Arjun Nair, Maja Rudolph, Marius Kloft:
TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection. Trans. Mach. Learn. Res. 2023 (2023) - [j22]Antoine Ledent
Orthogonal Inductive Matrix Completion. IEEE Trans. Neural Networks Learn. Syst. 34(5): 2259-2270 (2023) - [c62]Antoine Ledent, Rodrigo Alves, Yunwen Lei, Yann Guermeur, Marius Kloft:
Generalization Bounds for Inductive Matrix Completion in Low-Noise Settings. AAAI 2023: 8447-8455 - [c61]Phil Ostheimer, Mayank Kumar Nagda, Marius Kloft, Sophie Fellenz:
A Call for Standardization and Validation of Text Style Transfer Evaluation. ACL (Findings) 2023: 10791-10815 - [c60]Matthias Kirchler, Christoph Lippert, Marius Kloft:
Training Normalizing Flows from Dependent Data. ICML 2023: 17105-17121 - [c59]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. ICML 2023: 19882-19910 - [c58]Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne:
Labeling Neural Representations with Inverse Recognition. NeurIPS 2023 - [c57]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Zero-Shot Anomaly Detection via Batch Normalization. NeurIPS 2023 - [c56]Billy Joe Franks
Ordinal Regression for Difficulty Prediction of StepMania Levels. ECML/PKDD (6) 2023: 497-512 - [i53]Billy Joe Franks, Benjamin Dinkelmann, Sophie Fellenz, Marius Kloft:
Ordinal Regression for Difficulty Estimation of StepMania Levels. CoRR abs/2301.09485 (2023) - [i52]Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, Maja Rudolph:
Deep Anomaly Detection under Labeling Budget Constraints. CoRR abs/2302.07832 (2023) - [i51]Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt:
Zero-Shot Anomaly Detection without Foundation Models. CoRR abs/2302.07849 (2023) - [i50]Fabian Hartung, Billy Joe Franks, Tobias Michels, Dennis Wagner, Philipp Liznerski, Steffen Reithermann, Sophie Fellenz, Fabian Jirasek, Maja Rudolph, Daniel Neider, Heike Leitte, Chen Song
Deep Anomaly Detection on Tennessee Eastman Process Data. CoRR abs/2303.05904 (2023) - [i49]Phil Ostheimer, Mayank Nagda, Marius Kloft, Sophie Fellenz:
A Call for Standardization and Validation of Text Style Transfer Evaluation. CoRR abs/2306.00539 (2023) - [i48]Phil Ostheimer, Mayank Nagda, Marius Kloft, Sophie Fellenz:
Text Style Transfer Evaluation Using Large Language Models. CoRR abs/2308.13577 (2023) - [i47]Charu James, Mayank Nagda, Nooshin Haji Ghassemi, Marius Kloft, Sophie Fellenz:
Evaluating Dynamic Topic Models. CoRR abs/2309.08627 (2023) - [i46]Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne:
Labeling Neural Representations with Inverse Recognition. CoRR abs/2311.13594 (2023) - 2022
- [j21]Matthias Kirchler
transferGWAS: GWAS of images using deep transfer learning. Bioinform. 38(14): 3621-3628 (2022) - [j20]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. Trans. Mach. Learn. Res. 2022 (2022) - [j19]Jiyuan Liu
Multiview Subspace Clustering via Co-Training Robust Data Representation. IEEE Trans. Neural Networks Learn. Syst. 33(10): 5177-5189 (2022) - [c55]Waleed Mustafa, Yunwen Lei, Marius Kloft:
On the Generalization Analysis of Adversarial Learning. ICML 2022: 16174-16196 - [c54]Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt:
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. ICML 2022: 18153-18167 - [c53]Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph:
Raising the Bar in Graph-level Anomaly Detection. IJCAI 2022: 2196-2203 - [i45]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) - [i44]Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt:
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. CoRR abs/2202.08088 (2022) - [i43]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) - [i42]Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph:
Raising the Bar in Graph-level Anomaly Detection. CoRR abs/2205.13845 (2022) - [i41]Matthias Kirchler, Christoph Lippert, Marius Kloft:
Training Normalizing Flows from Dependent Data. CoRR abs/2209.14933 (2022) - [i40]Ajay Chawda, Stefanie Grimm, Marius Kloft:
Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings. CoRR abs/2210.14056 (2022) - [i39]Antoine Ledent, Rodrigo Alves
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings. CoRR abs/2212.08339 (2022) - 2021
- [j18]Xinwang Liu
Efficient and Effective Regularized Incomplete Multi-View Clustering. IEEE Trans. Pattern Anal. Mach. Intell. 43(8): 2634-2646 (2021) - [j17]Lukas Ruff
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
Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks. AAAI 2021: 8279-8287 - [c50]Liang Wu, Antoine Ledent, Yunwen Lei
Fine-grained Generalization Analysis of Vector-Valued Learning. AAAI 2021: 10338-10346 - [c49]Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen
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
Learning Interpretable Concept Groups in CNNs. IJCAI 2021: 1061-1067 - [c46]Waleed Mustafa, Yunwen Lei
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
Burst-induced Multi-Armed Bandit for Learning Recommendation. RecSys 2021: 292-301 - [i38]Chen Qiu
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
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
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
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
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
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
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
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
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
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
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
Huber-Norm Regularization for Linear Prediction Models. ECML/PKDD (1) 2016: 714-730 - [c25]Dominik Herrmann, Matthias Kirchler
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
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
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
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
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
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
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
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