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Creators/Authors contains: "Erdogdu, M A"

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  1. ; ; (, The Thirteenth International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available May 15, 2026
  2. ; ; (, The Thirteenth International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available May 15, 2026
  3. ; ; ; ; (, Proceedings of Thirty Sixth Conference on Learning Theory)
  4. ; ; ; ; (, Proceedings of Thirty Sixth Conference on Learning Theory)
  5. ; ; (, Advances in Neural Information Processing Systems 33 (NeurIPS 2020))
    null (Ed.)
    The randomized midpoint method, proposed by (Shen and Lee, 2019), has emerged as an optimal discretization procedure for simulating the continuous time underdamped Langevin diffusion. In this paper, we analyze several probabilistic properties of the randomized midpoint discretization method, considering both overdamped and underdamped Langevin dynamics. We first characterize the stationary distribution of the discrete chain obtained with constant step-size discretization and show that it is biased away from the target distribution. Notably, the step-size needs to go to zero to obtain asymptotic unbiasedness. Next, we establish the asymptotic normality of numerical integration using the randomized midpoint method and highlight the relative advantages and disadvantages over other discretizations. Our results collectively provide several insights into the behavior of the randomized midpoint discretization method, including obtaining confidence intervals for numerical integrations. 
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    Accepted Manuscript Full Text Available
  6. ; ; ; ; (, Advances in neural information processing systems)
    Accepted Manuscript Full Text Available