• 630
    Citations - based on content available in repository [source: Scopus]
20102023

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Personal profile

Academic background

Robert Peharz received his Master degree in Computer Engineering from Graz University of Technology (TU Graz), Austria (2010). From 2010-2015, he pursued his PhD studies at TU Graz, working on probabilistic graphcial models and sum-product networks, with applications to signal processing. From 2015-2017, he was postdoc at the Medical University of Graz, working on interdisciplinary approaches for early recognition of neural maldevelopment via behavioral neuroscience. He was postdoc in the Machine Learning Group (MLG) at the University of Cambridge from 2017-2018. He was Marie-Curie Individual Fellow at MLG Cambridge from 2018-2019. Robert joined Eindhoven University of Technology (TU/e) in November 2019 as an Assistant Professor in the Artificial Intelligence cluster.

Quote

Uncertainty Matters

Research profile

Robert Peharz is an Assistant Professor in the Artificial Intelligence cluster at Eindhoven University of Technology. Robert's work leverages probability as a principled language to represent and process uncertain knowledge. His main research activity is dedicated to develop powerful and expressive machine learning algorithms which are based on probabilistic principles. His particular research targets are probabilistic graphical models, tractable probabilistic models such as probabilistic circuits, and probabilistic deep learning. In his work, Robert aims to unite principled probabilistic modeling with the power of the entire machine learning toolbox.

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 7 - Affordable and Clean Energy

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Collaborations and top research areas from the last five years

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  • Bayesian Structure Scores for Probabilistic Circuits

    Yang, Y., Gala, G. & Peharz, R., 2023, International Conference on Artificial Intelligence and Statistics. PMLR, p. 563-575 13 p. (Proceedings of Machine Learning Research; vol. 206).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • Continuous mixtures of tractable probabilistic models

    Correia, A. H. C., Gala, G., Quaeghebeur, E., de Campos, C. P. & Peharz, R., 26 Jun 2023, Proceedings of the 37th AAAI Conference on Artificial Intelligence. Williams, B., Chen, Y. & Neville, J. (eds.). AAAI Press, p. 7244-7252 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 37, no. 6).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Open Access
    File
    3 Citations (Scopus)
    14 Downloads (Pure)
  • PS3: Partition-Based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data

    Fajri, R., Khoshrou, S., Peharz, R. & Pechenizkiy, M., 2021, Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings. Dong, Y., Mladenic, D. & Saunders, C. (eds.). Springer, p. 68-84 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12461 LNAI).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    1 Citation (Scopus)
  • Deep Structured Mixtures of Gaussian Processes

    Trapp, M., Peharz, R., Pernkopf, F. & Rasmussen, C. E., 26 Apr 2020, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (PMLR). Chiappa, S. & Calandra, R. (eds.). p. 2251-2261 11 p. (Proceedings of Machine Learning Research (PMLR); vol. 108).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Open Access
    17 Citations (Scopus)
  • Joints in Random Forests

    Correia, A., Peharz, R. & de Campos, C. P., 2020, Advances in Neural Information Processing Systems. Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F. & Lin, H. (eds.). Curran Associates, p. 11404-11415 12 p. (Advances in Neural Information Processing Systems; no. 33).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Open Access
    17 Citations (Scopus)