Achieving the Kesten-Stigum bound in the non-uniform hypergraph stochastic block model

Collection of machine-learning and statistical theory papers covering advances in graph clustering, dynamical-system emulation, online inference, semisupervised estimation, system identification, nonstationary bandits, conformal prediction, theory of deep learning, Bayesian experimental design, and applied actuarial modeling. Key technical contributions include: (1) a Kesten–Stigum-type threshold and a polynomial-time spectral algorithm using an optimally weighted non-backtracking operator for non-uniform hypergraph stochastic block models; (2) adversarial optimal-transport regularizers (Sinkh