Publications

Articles

  • Sing T*, Hoefling H*, Hossain I, Boisclair J, Doelemeyer A, Flandre T, et al. A deep learning-based model of normal histology. bioRxiv. 2019;838417. Online (* the authors contributed equally)
  • Mueller A, Hoefling HA, Muaremi A, Praestgaard J, Walsh LC, Bunte O, et al. Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial. JMIR mHealth and uHealth. 2019;7:e15191. Online
  • Keppler AM, Nuritidinow T, Mueller A, Hoefling H, Schieker M, Clay I, et al. Validity of accelerometry in step detection and gait speed measurement in orthogeriatric patients. PLOS ONE. 2019;14:e0221732. Online
  • Mueller A, Hoefling H, Nuritdinow T, Holway N, Schieker M, Daumer M, et al. Continuous Monitoring of Patient Mobility for 18 Months Using Inertial Sensors following Traumatic Knee Injury: A Case Study. Digital Biomarkers. 2018;79–89. Online
  • Viallon V, Lambert-Lacroix S, Höfling H, Picard F. On the robustness of the generalized fused lasso to prior specifications. Stat Comput. 2016;26:285–301. Online
  • Hoefling H, Rossini A. Reproducible Research for large scale data analysis. Implementing Reproducible Research. New York: Chapman and Hall/CRC; 2014. p. 219–40.
  • Höfling H, Eckert C, Schumacher M. “Classification of Therapy Resistance Based on Longitudinal Biomarker Profiles” by M. Kohlmann, L. Held and V. P. Grunert Biometrical Journal (2009) 51(4):610–626 Article. Authors’ reply. 2010;52:562–6. Online
  • Höfling H, Binder H, Schumacher M. A coordinate-wise optimization algorithm for the Fused Lasso. 2010; Online
  • Hoefling H. A Path Algorithm for the Fused Lasso Signal Approximator. Journal of Computational and Graphical Statistics. 2010;19:984–1006. Online
  • Höfling H, Tibshirani R. Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods. J Mach Learn Res. 2009;10:883–906. Online
  • Höfling H, Wasserman L. Discussion of: “Statistical analysis of an archeological find” by Andrey Feuerverger. AOAS. 2008;2:77–83. Online
  • Hofling and Wasserman - Discussion of Statistical analysis of an archeolo.pdf.
  • Höfling H, Tibshirani R. A study of pre-validation. The Annals of Applied Statistics. 2008;2:643–64. Online
  • Getz G, Hofling H, Mesirov JP, Golub TR, Meyerson M, Tibshirani R, et al. Comment on “The Consensus Coding Sequences of Human Breast and Colorectal Cancers.” Science. 2007;317:1500–1500. Online
  • Friedman J, Hastie T, Höfling H, Tibshirani R. Pathwise coordinate optimization. The Annals of Applied Statistics. 2007;1:302–32. Online
  • Höfling H, Kiesel R, Löffler G. Understanding the Corporate Bond Yield Curve. The Pension Forum. 2004;15:2–34.

Posters

  • Sing T, Hossain I, Höfling H, Doelemeyer A, Saravanan C, Piaia A, et al. A deep learning-based model of normal histology. New York, USA; 2018. Presented at the Digital Pathology & AI Congress, New York, USA, 2018.

Talks

  • Höfling H. Machine Learning on pathology images. Hinxton, UK; 2018. Conference: EMBL-EBI workshop on “Machine Learning in Drug Discovery and Precision Medicine”