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ixAutoMLInteractive and Explainable Human-Centered AutoML

Project: Research

Participants

11 - 16 out of 16Page size: 10

Publications

  1. 2023

  2. E-pub ahead of print

    Symbolic Explanations for Hyperparameter Optimization

    Segel, S., Graf, H., Tornede, A., Bischl, B. & Lindauer, M., 16 May 2023, (E-pub ahead of print) AutoML Conference 2023.

    Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

  3. E-pub ahead of print

    Extended Abstract: Hyperparameters in Reinforcement Learning and How To Tune Them

    Eimer, T., Lindauer, M. & Raileanu, R., 2023, (E-pub ahead of print) The 16th European Workshop on Reinforcement Learning (EWRL 2023).

    Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

  4. 2022

  5. E-pub ahead of print

    Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution

    Moosbauer, J., Casalicchio, G., Lindauer, M. & Bischl, B., 11 Jun 2022, (E-pub ahead of print).

    Research output: Working paper/PreprintPreprint

  6. E-pub ahead of print

    Practitioner Motives to Use Different Hyperparameter Optimization Methods

    Hasebrook, N., Morsbach, F., Kannengießer, N., Zöller, M., Franke, J., Lindauer, M., Hutter, F. & Sunyaev, A., 3 Mar 2022, (E-pub ahead of print) In: ACM Transactions on Computer-Human Interaction.

    Research output: Contribution to journalArticleResearchpeer review

  7. Published

    PriorBand: HyperBand + Human Expert Knowledge

    Mallik, N., Hvarfner, C., Stoll, D., Janowski, M., Bergman, E., Lindauer, M. T., Nardi, L. & Hutter, F., 2022, 2022 NeurIPS Workshop on Meta Learning (MetaLearn).

    Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

  8. Published

    π BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization.

    Hvarfner, C., Stoll, D., Souza, A. L. F., Lindauer, M., Hutter, F. & Nardi, L., 2022, Proceedings of the International conference on Learning Representation (ICLR).

    Research output: Chapter in book/report/conference proceedingConference contributionResearch

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