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Award ID contains: 2233152

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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. ; ; ; (, Mathematics of Operations Research)
    We consider the infinite-horizon, average-reward restless bandit problem in discrete time. We propose a new class of policies that are designed to drive a progressively larger subset of arms toward the optimal distribution. We show that our policies are asymptotically optimal with an [Formula: see text] optimality gap for an N-armed problem, assuming only a unichain and aperiodicity assumption. Our approach departs from most existing work that focuses on index or priority policies, which rely on the Global Attractor Property to guarantee convergence to the optimum, or a recently developed simulation-based policy, which requires a Synchronization Assumption. 
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    Free, publicly-accessible full text available December 11, 2026
  3. ; (, Neural Information Processing Systems (NeurIPS) 2025)
    Free, publicly-accessible full text available December 1, 2026
  4. ; ; (, Neural Information Processing Systems (NeurIPS))
    Free, publicly-accessible full text available December 1, 2026
  5. ; (, Conference on Learning Theory (COLT), 2025.)
    Free, publicly-accessible full text available June 30, 2026
  6. ; ; (, International Conference on Machine Learning (ICML))
    Free, publicly-accessible full text available June 13, 2026
  7. ; ; ; (, International Conference on Machine Learning (ICML),)
    Free, publicly-accessible full text available June 13, 2026
  8. ; ; ; (, Neural Information Processing Systems (NeurIPS), 2025)
    Free, publicly-accessible full text available June 13, 2026
  9. ; ; (, Innovations in Theoretical Computer Science (ITCS), 2025.)
    Free, publicly-accessible full text available January 31, 2026
  10. ; ; ; (, International Conference on Artificial Intelligence and Statistics (AISTATS), 2025)
    Free, publicly-accessible full text available January 31, 2026