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..
Linear Bandits with Feature Feedback
Authors: Urvashi Oswal, Aniruddha Bhargava, Robert Nowak5331-5338
AAAI 2020 | Venue PDF | 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. |