Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Optimal randomized multilevel Monte Carlo for repeatedly nested expectations
Authors: Yasa Syed, Guanyang Wang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithm on three examples. Our comparison result is summarized in Figure 1. |
| Researcher Affiliation | Academia | 1Department of Statistics, Rutgers University, New Brunswick, United States. |
| Pseudocode | Yes | Algorithm 1 A recursive r MLMC algorithm for RNEs |
| Open Source Code | Yes | Our code is available at https://github. com/guanyangwang/r MLMC_RNE. |
| Open Datasets | No | The paper uses synthetic data generated from specified distributions and financial models (e.g., 'y(0) N(π/2, 1)', 'non-central t-distribution'), but does not utilize or provide access information for any publicly available datasets. |
| Dataset Splits | No | The paper does not provide details on specific train/validation/test dataset splits. Experiments are conducted on simulated processes or financial models rather than standard datasets with such splits. |
| Hardware Specification | No | The paper mentions running experiments on a '500-core cluster' but does not provide specific hardware details such as CPU/GPU models, memory, or exact cluster specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | For READ, since all assumptions in Theorem 2.2 are satisfied, therefore when r0 (1/2, 3/4) and r1 (1/2, 1 2 4/3), the READ estimator generated by Algorithm 1 is unbiased and of finite variance. Since the computational cost gets lower when each ri gets larger, we choose r0 = 0.74 and r1 = 0.6 (close to the upper-end of their respective ranges above) to facilitate the computational efficiency. We also adopt the standard parameters in (Jain & Oosterlee, 2012; Bender et al., 2006; Zhou et al., 2022): T = 3, M = 5, σ = 0.2, r = 0.05, K = y(0) i = 100 for every i. |