Linear Bandits with Feature Feedback
Authors: Urvashi Oswal, Aniruddha Bhargava, Robert Nowak5331-5338
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of the algorithm with synthetic and real human-labeled data. |
| Researcher Affiliation | Collaboration | 1Oracle Labs, Redwood Shores, CA 2University of Wisconsin-Madison, Madison, WI |
| Pseudocode | Yes | Algorithm 1 OFUL; Algorithm 2 Feature Feedback OFUL (FF-OFUL) |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code for the described methodology or links to a code repository. |
| Open Datasets | Yes | We use the 20Newsgroup (20NG) dataset from (Lang 1995). |
| Dataset Splits | No | The paper describes the datasets used (synthetic and 20Newsgroup) but does not provide specific train/validation/test splits (e.g., percentages, counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For OFUL the ridge parameter (λ) is tuned from {2i}10 i= 7 to pick the one with best performance. All the tuned parameters selected for OFUL were strictly inside this range (for d = 40, k = 5 , λ = 2 5 and for d = 103 (Newsgroup), λ = 28). For FF-OFUL, the remarkable feature is that it does not require parameter tuning so λ = 1 for all experiments. |