Online control of the false discovery rate with decaying memory

Authors: Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael I. Jordan

NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Numerical simulations in Appendix C complement the theoretical results. We demonstrate using simulations that such accumulated wealth can lead to a spike in the false discovery proportion locally in time.
Researcher Affiliation Academia Aaditya Ramdas Fanny Yang Martin J. Wainwright Michael I. Jordan University of California, Berkeley {aramdas, fanny-yang, wainwrig, jordan} @berkeley.edu
Pseudocode No The information is insufficient. The paper describes algorithms and rules using mathematical notation and prose, but it does not include a clearly labeled figure, block, or section titled 'Pseudocode' or 'Algorithm'.
Open Source Code No The information is insufficient. The paper does not include any statements about releasing source code for the described methodology or provide a direct link to a code repository.
Open Datasets No The information is insufficient. The paper describes numerical simulations but does not mention the use of any publicly available or open datasets by name, link, or citation for training or evaluation purposes.
Dataset Splits No The information is insufficient. The paper discusses numerical simulations but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits).
Hardware Specification No The information is insufficient. The paper does not explicitly describe any specific hardware components (e.g., GPU/CPU models, memory details, or cloud instance types) used for running its experiments or simulations.
Software Dependencies No The information is insufficient. The paper does not provide specific software dependency details, such as library or solver names with version numbers, needed to replicate the experiments.
Experiment Setup No The information is insufficient. The paper introduces theoretical algorithms and refers to numerical simulations in Appendix C, but the main text does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings.