Boosting with Online Binary Learners for the Multiclass Bandit Problem
Authors: Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several real-world data sets demonstrate the effectiveness of the proposed approach. In addition, the algorithm reaches promising empirical performance on real-world data sets, even when using very simple full-information weak learners. |
| Researcher Affiliation | Academia | Shang-Tse Chen SCHEN351@GATECH.EDU School of Computer Science, Georgia Institute of Technology, Atlanta, GA Hsuan-Tien Lin HTLIN@CSIE.NTU.EDU.TW Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan Chi-Jen Lu CJLU@IIS.SINICA.EDU.TW Institute of Information Science, Academia Sinica, Taipei, Taiwan |
| Pseudocode | Yes | Algorithm 1 Bandit boosting algorithm with online weak learner WL |
| Open Source Code | No | The paper does not provide any statement about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | We test our algorithm on 5 public real-world data sets from various domains with different sizes: CAR, NURSERY, and CONNECT4 from the UCI machine learning repository (Frank & Asuncion, 2010); DNA from the Statlog project (Michie et al., 1994); REUTERS4 from the paper of Banditron (Kakade et al., 2008). |
| Dataset Splits | No | The paper describes an online learning setting where examples are processed sequentially, and the error rate is calculated based on cumulative prediction errors. It does not provide explicit training, validation, or test dataset splits in terms of pre-partitioned subsets of the data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Perceptron', 'Naive Bayes', 'Banditron', and 'C-OVA' but does not specify their version numbers or any other software dependencies with versioning information. |
| Experiment Setup | Yes | For fairness of comparison to Banditron, we do not tune the parameters other than the exploration rate δ. We fix the number of weak learners to be 100 and the assumed weak learner advantage γ to be 0.1 as in the full-information online boosting algorithm (Chen et al., 2012). For the exploration rate δ, we test a wide range of values to see the effect of random exploration. |