New Paper Accepted in IEEE Open Journal of Signal Processing

Congratulations to R. Money, J. Krishnan (SimulaMet), B. Beferull-Lozano, E. Isufi (Delft University) for the acceptance of a journal paper in IEEE Open Journal of Signal Processing.

R. Money, J. Krishnan, B. Beferull-Lozano, E. Isufi, “Scalable and Privacy-aware Online Learning of Non-linear Structural Equation Models”, IEEE Open Journal of Signal Processing, 2023.

Figure: Dependencies between companies in stock market (S&P 500 index) during and after COVID

Title: Scalable and Privacy-aware Online Learning of Non-linear Structural Equation Models

Short description of the paper:
 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 correction technique that accounts for the model evolution. 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.