World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

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

Marius Kloft publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Marius Kloft sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 250 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 560 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 151 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Marius Kloft D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Marius Kloft sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring Computer Science opens doors to several related online degree programs. Many students consider branching into fields such as mechanical engineering, physics, data science, or electrical engineering. Each pathway offers distinct career possibilities and educational experiences.

Choosing the right degree often depends on factors like cost and career outcomes. If affordability is a top concern, look into the mechanical engineering degree online cost and the options for obtaining an online physics bachelor's degree. Both programs can build a strong technical foundation at competitive tuition rates.

As the demand for tech specialists grows, practical fields like data science and electrical engineering are also popular choices. You can find an affordable data science degree online or explore the promising online electrical engineering career outcomes available in the USA.

These related degrees offer flexible study options and versatile career pathways for students with a passion for technology and innovation.

Best Scientists Citing Marius Kloft

Trending Scientists

Recently Published Articles