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D-Index & Metrics

Computer Science

D-Index
32
Citations
5755
World Ranking
12993
National Ranking
632

Overview

Marius Kloft is affiliated with the Technical University of Kaiserslautern in Germany. Their research primarily focuses on the field of Computer Science, with significant contributions in several subfields and topics related to artificial intelligence and machine learning.

Their main subfields of study include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering
  • Computer Networks and Communications
  • Industrial and Manufacturing Engineering

Key research topics covered in their work are:

  • Anomaly Detection Techniques and Applications
  • Adversarial Robustness in Machine Learning
  • Network Security and Intrusion Detection
  • Face and Expression Recognition
  • Sparse and Compressive Sensing Techniques
  • Explainable Artificial Intelligence (XAI)
  • Data-Driven Disease Surveillance

Frequent coauthors collaborating with Marius Kloft include:

  • Sophie Fellenz
  • Stephan Mandt
  • Maja Rudolph
  • Philipp Liznerski
  • Antoine Ledent

Their publications appear in a variety of venues, with multiple papers in the following:

  • arXiv (Cornell University)
  • Chemie Ingenieur Technik
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Procedia CIRP
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Representative recent papers authored or co-authored by Marius Kloft include:

  • Efficient and Effective Regularized Incomplete Multi-view Clustering, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Machine Learning in Chemical Engineering: A Perspective, 2021, Chemie Ingenieur Technik
  • Multiview Subspace Clustering via Co-Training Robust Data Representation, 2021, IEEE Transactions on Neural Networks and Learning Systems
  • Explainable Deep One-Class Classification, 2020, arXiv (Cornell University)
  • Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion, 2020, The Journal of Physical Chemistry Letters

Best Publications

  • A Unifying Review of Deep and Shallow Anomaly Detection

    Lukas Ruff;Jacob R. Kauffmann;Robert A. Vandermeulen;Gregoire Montavon

  • Deep One-Class Classification

    Lukas Ruff;Robert Vandermeulen;Nico Goernitz;Lucas Deecke

  • Toward supervised anomaly detection

    Nico Görnitz;Marius Kloft;Konrad Rieck;Ulf Brefeld

  • l p -Norm Multiple Kernel Learning

    Marius Kloft;Ulf Brefeld;Sören Sonnenburg;Alexander Zien

  • Predicting MOOC Dropout over Weeks Using Machine Learning Methods

    Marius Kloft;Felix Stiehler;Zhilin Zheng;Niels Pinkwart

  • Efficient and Accurate Lp-Norm Multiple Kernel Learning

    Marius Kloft;Ulf Brefeld;Pavel Laskov;Klaus-Robert Müller

  • Multiple Kernel $k$ k -Means with Incomplete Kernels

    Xinwang Liu;Xinzhong Zhu;Miaomiao Li;Lei Wang

  • Efficient and Effective Regularized Incomplete Multi-View Clustering

    Xinwang Liu;Miaomiao Li;Chang Tang;Jingyuan Xia

  • Machine Learning in Chemical Engineering: A Perspective

    Artur M. Schweidtmann;Artur M. Schweidtmann;Erik Esche;Asja Fischer;Marius Kloft

  • Image Anomaly Detection with Generative Adversarial Networks

    Lucas Deecke;Robert A. Vandermeulen;Lukas Ruff;Stephan Mandt

  • Online Anomaly Detection under Adversarial Impact

    Marius Kloft;Pavel Laskov

  • Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events

    Guang Yu;Siqi Wang;Zhiping Cai;En Zhu

  • Deep Semi-Supervised Anomaly Detection

    Lukas Ruff;Robert A. Vandermeulen;Nico Görnitz;Alexander Binder

  • Multiview Subspace Clustering via Co-Training Robust Data Representation.

    Jiyuan Liu;Xinwang Liu;Yuexiang Yang;Xifeng Guo

  • Mixed kernel based extreme learning machine for electric load forecasting

    Yanhua Chen;Yanhua Chen;Marius Kloft;Yi Yang;Caihong Li

  • Learning Kernels Using Local Rademacher Complexity

    Corinna Cortes;Marius Kloft;Mehryar Mohri

  • Explainable Deep One-Class Classification

    Philipp Liznerski;Lukas Ruff;Robert A. Vandermeulen;Billy Joe Franks

  • Active learning for network intrusion detection

    Nico Görnitz;Marius Kloft;Konrad Rieck;Ulf Brefeld

  • Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion.

    Fabian Jirasek;Fabian Jirasek;Rodrigo A. S. Alves;Julie Damay;Robert A. Vandermeulen

  • Security analysis of online centroid anomaly detection

    Marius Kloft;Pavel Laskov

  • Explainable Deep One-Class Classification

    Philipp Liznerski;Lukas Ruff;Robert A. Vandermeulen;Billy Joe Franks

  • Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network

    Siqi Wang;Yijie Zeng;Xinwang Liu;En Zhu

  • A framework for quantitative security analysis of machine learning

    Pavel Laskov;Marius Kloft

  • Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

    Bettina Mieth;Marius Kloft;Juan Antonio Rodríguez;Sören Sonnenburg

Frequent Co-Authors

Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Gunnar Rätsch
Gunnar Rätsch ETH Zurich
Pavel Laskov
Pavel Laskov University of Liechtenstein
Xinwang Liu
Xinwang Liu National University of Defense Technology
Konrad Rieck
Konrad Rieck Technische Universität Braunschweig
Robert Jenssen
Robert Jenssen University of Tromsø - The Arctic University of Norway
John P. Cunningham
John P. Cunningham Columbia University
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
Thomas G. Dietterich
Thomas G. Dietterich Oregon State University
Alexander Mitsos
Alexander Mitsos RWTH Aachen University

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