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..
CBRAP: Contextual Bandits with RAndom Projection
Authors: Xiaotian Yu, Michael R. Lyu, Irwin King
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By comparing with three benchmark algorithms, we demonstrate improved performance on cumulative payoffs of CBRAP during its sequential decisions on both synthetic and real-world datasets, as well as its superior time-efficiency. ... We evaluate the CBRAP algorithm via a series of experiments with synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Xiaotian Yu, Michael R. Lyu, Irwin King Department of Computer Science and Engineering The Chinese University of Hong Kong, Shatin, N.T., Hong Kong Email: EMAIL |
| Pseudocode | Yes | Algorithm 1 CBRAP |
| Open Source Code | Yes | Our algorithm and used datasets are all publicly available1 1https://github.com/Aaronyxt/CBRAP |
| Open Datasets | Yes | Then, we conduct experiments on two real-world datasets, i.e., Movielens2 and Jester3. ... 2http://grouplens.org/datasets/movielens/ 3http://www.ieor.berkeley.edu/ goldberg/jester-data/ |
| Dataset Splits | No | The paper describes the datasets used and the performance metric (cumulative payoffs over T=1000 rounds) but does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | Yes | We conduct all experiments on a server installed with Ubuntu 12.04.5 LTS, which contains 24 processors of each core being Intel CPU@2.60GHz, and has a total memory of 200GB. |
| Software Dependencies | No | The paper mentions 'Ubuntu 12.04.5 LTS' as the operating system, but does not provide specific version numbers for any other software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | No | The paper mentions inputs like 'm, T, β R+ and α R+' for Algorithm 1 and explores different values of 'm' in experiments (m = 10, 20, 30, 40, 50), but it does not specify concrete hyperparameters like learning rates, batch sizes, or other training configurations. |