Publications

  1. Dewolf, N. (2024). A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning (Doctoral dissertation). Ghent University. http://hdl.handle.net/1854/LU-01HVK8CNHPMDFGQ8BGF86VMVTY
    (Note: The Arxiv version can be found here.)

  2. Vancraeynest-De Cuiper, B., Bridgeman, J., Dewolf, N., Haegeman, J. & Verstraete, F. (2023). One-dimensional symmetric phases protected by frieze symmetries. Phys. Rev. B. https://link.aps.org/doi/10.1103/PhysRevB.107.115123

  3. Dewolf, N., Baets, B.D. & Waegeman, W. (2023). Valid prediction intervals for regression problems. Artif Intell Rev. https://doi.org/10.1007/s10462-022-10178-5
    (Note: Due to a small typesetting error1, the second author appears as “Baets, B.D.” while it should be “De Baets, B.”)

  4. Dewolf, N. (2019). Spatial Symmetries and Symmetry Breaking with Matrix Product States. (Master’s thesis). Ghent University. https://lib.ugent.be/en/catalog/rug01:002782900

Preprints:

  1. Dewolf, N., De Baets, B. & Waegeman, W. (2023). Conditional validity of heteroskedastic conformal regression. https://arxiv.org/abs/2309.08313

My Google Scholar page is Nicolas Dewolf.
My Arxiv page is Dewolf, N.


Conferences

  1. Dewolf, N., De Baets, B., and Waegeman, W. (2023). Conditional Conformal Prediction. Workshop on Uncertainty in Machine Learning (WUML).

  2. Dewolf, N., De Baets, B., and Waegeman, W. (2022). Valid prediction intervals for regression problems. Joint International Scientific Conferences on AI and Machine Learning (BNAIC/BeNeLearn).

  3. Dewolf, N., De Baets, B., and Waegeman, W. (2021). Regression problems in machine learning. Data Science, Statistics & Visualisation (DSSV) and the European Conference on Data Analysis (ECDA).

  4. Dewolf, N., De Baets, B., and Waegeman, W. (2021). Calibrated prediction intervals. Workshop on Uncertainty in Machine Learning (WUML).

  1. The publisher was made aware of this issue before publication, but did not take our comment into account. ↩