@article{17145,
  author = {Rohan Money and Joshin Krishnan and Baltasar Beferull-Lozano and Elvin Isufi},
  title = {Scalable and Privacy-aware Online Learning of Nonlinear Structural Equation Models},
  abstract = {An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. \E{The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corroborate our findings and show competitive performance w.r.t. state-of-the-art alternatives.},
  year = {2022},
  journal = {IEEE Open Journal of Signal Processing},
  publisher = {IEEE},
  url = {https://ieeexplore.ieee.org/document/10034854},
  doi = {10.1109/OJSP.2023.3241580},
  note = {This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70  from the Research Council of Norway.},
}