Publications

Mark Bun, Marco Gaboardi, Marcel Neunhoeffer, Wanrong Zhang (2024). Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections. PODS 24.

Christian Arnold, Luka Biedebach, Andreas Küpfer, Marcel Neunhoeffer (2023). The Role of Hyperparameters in Machine Learning Models and How to Tune Them. PSRM.

Oliver Rittmann, Marcel Neunhoeffer, Thomas Gschwend (2023). How to Improve the Substantive Interpretation of Regression Results when the Dependent Variable is Logged. PSRM.

Marcel Neunhoeffer, Daniel Sheldon, Adam Smith (2023). Bootstrap-based, General-purpose Statistical Inference from Differential Private Releases. Working Paper.

Marcel Neunhoeffer, Zhiwei Steven Wu, Cynthia Dwork (2021). Private Post-GAN Boosting. ICLR.

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Marcel Neunhoeffer, Christian Arnold (2020). Really Useful Synthetic Data -- A Framework to Evaluate the Quality of Differentially Private Synthetic Data.

Marcel Neunhoeffer, Thomas Gschwend, Simon Munzert, Lukas F. Stoetzer (2020). An Approach to Predicting the District Vote Shares in German Federal Elections. PVS.

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Marcel Neunhoeffer, Lukas F. Stoetzer, Thomas Gschwend, Simon Munzert, Sebastian Sternberg (2019). Forecasting Elections in Multi-Party Systems: A Bayesian Approach Combining Polls and Fundamentals. Political Analysis.

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Marcel Neunhoeffer, Sebastian Sternberg (2019). How Cross-Validation Can Go Wrong and What to Do About it.. Political Analysis.

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Marcel Neunhoeffer, Lukas F. Stoetzer, Thomas Gschwend, Simon Munzert, Sebastian Sternberg (2017). Zweitstimme.org. A structural-dynamic forecasting model for German federal elections. PVS.

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Marcel Neunhoeffer (2016). A Partisan Treatment in a High Salience Election: Evidence from a Field Experiment in Germany. Working Paper.

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