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Non-asymptotic analysis of Langevin-type Monte Carlo algorithms
We study Langevin-type algorithms for sampling from Gibbs distributions such that the potentials are dissipative and their weak gradients have finite moduli of continuity not necessarily convergent to zero.
Benign Overfitting in Time Series Linear Model with Over-Parameterization
Then, we derive bounds of risks by the estimator for the cases where the temporal correlation of each coordinate of dependent data is homogeneous and heterogeneous, respectively.
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