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..
Sequential Experimental Design for Transductive Linear Bandits
Authors: Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian Ratliff
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present simulations for the linear bandit pure exploration problem and the general transductive bandit problem. We compare our proposed algorithm with both adaptive and nonadaptive strategies. |
| Researcher Affiliation | Academia | Tanner Fiez Electrical & Computer Engineering University of Washington ... Lalit Jain Allen School of Computer Science & Engineering University of Washington ... Kevin Jamieson Allen School of Computer Science & Engineering University of Washington ... Lillian Ratliff Electrical & Computer Engineering University of Washington |
| Pseudocode | Yes | Algorithm 1: RAGE(X, Z, , r( ), δ): Randomized Adaptive Gap Elimination |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | To conduct an experiment based on real data, we build a problem using the Yahoo! Webscope Dataset R6A.5 ... 5https://webscope.sandbox.yahoo.com/ |
| Dataset Splits | No | The paper describes problem setups and simulations but does not provide specific details on train/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments or simulations. |
| Software Dependencies | No | The paper does not provide specific software dependency names with version numbers required to replicate the experiment. |
| Experiment Setup | Yes | We run each algorithm at a confidence level of δ = 0.05. To compute the samples for RAGE, we first used the Frank-Wolfe algorithm (with a precise stopping condition in the supplementary) to find λt, and then the rounding procedure from [27] with = 1/10. ... We also include known parameters 1 = 1 and 2 = 0.5 ... The weights of the parameter vector are drawn from a discrete uniform distribution with a range of [ 0.3, 0.3] and a granularity of 0.01. ... We then fit a regularized least squares estimate using a regularization parameter of 0.01 to obtain . |