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Researcher profile

Bernd Bischl

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Time series anomaly detection based on shapelet learning

    2018 · Computational Statistics

    We consider the problem of learning to detect anomalous time series from an unlabeled data set, possibly contaminated with anomalies in the training data. This scenario is important for applications in medicine, economics, or industrial …

  2. Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift

    2019 · arXiv (Cornell University)

    A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution shift. We use Auto Encoding Variational Bayes to find a latent …

  3. Mutation is all you need

    2021 · arXiv (Cornell University)

    Neural architecture search (NAS) promises to make deep learning accessible to non-experts by automating architecture engineering of deep neural networks. BANANAS is one state-of-the-art NAS method that is embedded within the Bayesian optimization framework. Recent …

  4. Automatic Componentwise Boosting: An Interpretable AutoML System

    2021 · arXiv (Cornell University)

    In practice, machine learning (ML) workflows require various different steps, from data preprocessing, missing value imputation, model selection, to model tuning as well as model evaluation. Many of these steps rely on human ML experts. …

  5. FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation

    2022 · arXiv (Cornell University)

    The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to quantify epistemic uncertainty, usable across …

  6. A Guide to Feature Importance Methods for Scientific Inference

    2024 · Communications in computer and information science

    Abstract While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding the DGP requires insights into feature-target associations, which many …