Sequential Experimental Design for Transductive Linear Bandits
Authors: Tanner Fiez, Lalit Jain, Kevin G. Jamieson, Lillian Ratliff
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 . |