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  1. ; ; ; (, Mathematical Finance)
    Abstract We develop a convex‐optimization clustering algorithm for heterogeneous financial networks, in the presence of arbitrary or even adversarial outliers. In the stochastic block model with heterogeneity parameters, we penalize nodes whose degree exhibit unusual behavior beyond inlier heterogeneity. We prove that under mild conditions, this method achieves exact recovery of the underlying clusters. In absence of any assumption on outliers, they are shown not to hinder the clustering of the inliers. We test the performance of the algorithm on semi‐synthetic heterogenous networks reconstructed to match aggregate data on the Korean financial sector. Our method allows for recovery of sub‐sectors with significantly lower error rates compared to existing algorithms. For overlapping portfolio networks, we uncover a clustering structure supporting diversification effects in investment management. 
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  2. ; ; (, Innovations in Theoretical Computer Science (ITCS), 2025.)
    Free, publicly-accessible full text available January 31, 2026
  3. ; ; ; (, International Conference on Artificial Intelligence and Statistics (AISTATS), 2025)
    Free, publicly-accessible full text available January 31, 2026
  4. ; ; ; (, ACM SIGMETRICS 2025.)
    Free, publicly-accessible full text available January 30, 2026
  5. ; ; ; ; (, International Conference on Learning Representations (ICLR), 2025.)
    Free, publicly-accessible full text available January 22, 2026
  6. ; (, International Conference on Algorithmic Learning Theory (ALT), 2025.)
    Free, publicly-accessible full text available October 10, 2025
  7. ; ; ; (, 38th Conference on Neural Information Processing Systems (NeurIPS 2024))
    Free, publicly-accessible full text available September 25, 2025
  8. ; ; (, 38th Conference on Neural Information Processing Systems (NeurIPS 2024))
    Free, publicly-accessible full text available September 25, 2025
  9. ; (, 38th Conference on Neural Information Processing Systems (NeurIPS 2024))
    Free, publicly-accessible full text available September 25, 2025
  10. ; ; ; ; ; (, IEEE Transactions on Information Theory)
    Free, publicly-accessible full text available September 1, 2025