Difan Deng
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum
Difan Deng
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum

I am working towards a Ph.D. at Leibniz University Hannover. Previously I obtained my Master's degree in electrical engineering and information technology at TU Darmstadt and my bachelor's degree in electronics and information engineering at Huazhong University of Science in 2019 and 2015 respectively. 

My research interest is AutoML, including Hyperparameter Optimization and Neural Architecture Search. The goal is to provide easy-to-use AutoML systems that allow non-expert Machine learning users to work with machine learning problems at hand.

Research Interests

  • Hyperparameter Optimization
  • Neural Architecture
  • Time Series forecasting

    Curriculum Vitae

    • Education

      Since 2020, Ph.D. Candidate, University Hannover, Germany

      2016-2019, Master of Science, Electrical Engineering and Information Technology , TU Darmstadt, Germany

      2011-2015, Bachelor of Engineering, Electronic and Information Engineering, Huazhong University of Science and Technology

    Publications

    Zeige Ergebnisse 1 - 8 von 8

    2023


    Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A-L., Deng, D., & Lindauer, M. (2023). Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), Artikel e1484. https://doi.org/10.1002/widm.1484
    Ruhkopf, T., Mohan, A., Deng, D., Tornede, A., Hutter, F., & Lindauer, M. (2023). MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information. Transactions on Machine Learning Research. Vorabveröffentlichung online. https://openreview.net/forum?id=5aYGXxByI6
    Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2023). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Vorabveröffentlichung online. https://doi.org/10.48550/arXiv.2306.08107

    2022


    Deng, D., Karl, F., Hutter, F., Bischl, B., & Lindauer, M. (2022). Efficient Automated Deep Learning for Time Series Forecasting. In Proceedings of the European Conference on Machine Learning (ECML) https://doi.org/10.48550/arXiv.2205.05511
    Deng, D., & Lindauer, M. (2022). Searching in the Forest for Local Bayesian Optimization. In ECML/PKDD workshop on Meta-learning Vorabveröffentlichung online. https://arxiv.org/abs/2111.05834
    Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., & Hutter, F. (2022). SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. Journal of Machine Learning Research, 2022(23). https://arxiv.org/abs/2109.09831

    2021


    Guerrero-Viu, J., Hauns, S., Izquierdo, S., Miotto, G., Schrodi, S., Biedenkapp, A., Elsken, T., Deng, D., Lindauer, M., & Hutter, F. (2021). Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization. In ICML 2021 Workshop AutoML Vorabveröffentlichung online. https://arxiv.org/abs/2105.01015

    2020


    Awad, N., Shala, G., Deng, D., Mallik, N., Feurer, M., Eggensperger, K., Biedenkapp, A., Vermetten, D., Wang, H., Doerr, C., Lindauer, M., & Hutter, F. (2020). Squirrel: A Switching Hyperparameter Optimizer. Vorabveröffentlichung online. https://arxiv.org/abs/2012.08180