My research is focused on statistical learning theory and machine learning. This includes generalization bounds (mainly of the information-theoretic variety); the application of such bounds in various settings; conformal prediction; online learning; and active learning.

I am currently a postdoc at the University College London Centre for Artificial Intelligence, advised by Benjamin Guedj and supported by WASP. Prior to this, I completed my PhD at Chalmers University of Technology as part of the WASP Graduate School AI-track, supervised by Giuseppe Durisi, and studied Physics at the University of Gothenburg.

Preprints

• Generalization and Informativeness of Weighted Conformal Risk Control Under Covariate Shift
  Matteo Zecchin, Fredrik Hellström, Sangwoo Park, Shlomo Shamai (Shitz), Osvaldo Simeone.
  Preprint, 2025.

Book

• Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
  Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, Maxim Raginsky.
  Foundations and Trends® in Machine Learning, 2025.

Conference and journal papers

• Generalization and Informativeness of Conformal Prediction
  Matteo Zecchin, Sangwoo Park, Osvaldo Simeone, Fredrik Hellström.
  ISIT, 2024.
• Comparing Comparators in Generalization Bounds
  Fredrik Hellström, Benjamin Guedj.
  AISTATS, 2024.
• Adaptive Selective Sampling for Online Prediction with Experts
  Rui M. Castro, Fredrik Hellström, Tim van Erven.
  NeurIPS 2023.
• Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness
  Fredrik Hellström, Giuseppe Durisi.
  NeurIPS 2022.
• New Family of Generalization Bounds Using Samplewise Evaluated CMI
  Fredrik Hellström, Giuseppe Durisi.
  NeurIPS 2022.
• Fast-rate loss bounds via conditional information measures with applications to neural networks
  Fredrik Hellström, Giuseppe Durisi.
  ISIT 2021.
• Generalization bounds via information density and conditional information density
  Fredrik Hellström, Giuseppe Durisi.
  JSAIT 2020.
• Generalization error bounds via mth central moments of the information density
  Fredrik Hellström, Giuseppe Durisi.
  ISIT 2020.
• New constraints on inelastic dark matter from IceCube
  Riccardo Catena, Fredrik Hellström.
  JCAP 2018.

Other

• Information-Theoretic Generalization Bounds: Tightness and Expressiveness
  PhD Thesis, 2023.
• Machine Learning, Compression, and Understanding
  Popular science presentation, 2022.
• Data-Dependent PAC-Bayesian Bounds in the Random-Subset Setting with Applications to Neural Networks
  Fredrik Hellström, Giuseppe Durisi.
  ITR3 Workshop at ICML, 2021.