Differentially Private Contextual Linear Bandits

Authors: Roshan Shariff, Or Sheffet

NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments with a few variants of our algorithms are detailed in Section D of the supplementary material.
Researcher Affiliation Academia Roshan Shariff Department of Computing Science University of Alberta Edmonton, Alberta, Canada roshan.shariff@ualberta.ca Or Sheffet Department of Computing Science University of Alberta Edmonton, Alberta, Canada osheffet@ualberta.ca
Pseudocode Yes Algorithm 1 Linear UCB with Changing Perturbations
Open Source Code No The paper mentions experiments in supplementary material but does not provide an explicit statement or link for the source code of the methodology.
Open Datasets No The paper does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset.
Dataset Splits No The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning.
Hardware Specification No The paper does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments.
Software Dependencies No The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment.
Experiment Setup No The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text.