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 [1].
Adaptive Strategy for Stratified Monte Carlo Sampling
Authors: Alexandra Carpentier, Remi Munos, András Antos
JMLR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide finite-time bounds on the MSE of the estimate of the mean value. To the best of our knowledge, these are the first finite-time results for the problem of adaptive stratified Monte Carlo which target directly a usual loss measure (i.e., the total MSE). These consist of: (i) A distribution-dependent bound of order poly(̕λ 1 min) e O(n 3/2) that depends on the disparity λmin of the strata (a measure of the problem complexity defined in Equation 6 below), and which corresponds to a stationary regime where the budget n is large compared to this complexity. (ii) A distribution-free bound of order poly(K) e O(n 7/6) that does not depend on the disparity of the strata, and corresponds to a transitory regime where n is small compared to the problem complexity. (iii) The latter bound is sharpened to order poly(K) e O(n 4/3) when each arm distribution is symmetric. |
| Researcher Affiliation | Collaboration | Alexandra Carpentier EMAIL Statistical Laboratory Center for Mathematical Sciences Wilberforce Road CB3 0WB Cambridge, United Kingdom Remi Munos EMAIL Google Deep Mind London, UK Andr as Antos EMAIL Budapest University of Technology and Economics 3 M uegyetem rkp. 1111 Budapest, Hungary |
| Pseudocode | Yes | Figure 1: The pseudo-code of the MC-UCB algorithm. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on adaptive sampling strategies for Monte Carlo integration. It does not describe experiments using specific publicly available datasets or provide access information for any data. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation on datasets, thus no dataset splits (training/test/validation) are mentioned. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup or specific hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and describes an algorithm; it does not mention specific software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical analysis of an adaptive sampling strategy. It does not describe any empirical experimental setup, hyperparameters, or training configurations. |