CBRAP: Contextual Bandits with RAndom Projection
Authors: Xiaotian Yu, Michael R. Lyu, Irwin King
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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: {xtyu,lyu,king}@cse.cuhk.edu.hk |
| 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. |