Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret
Authors: Alina Beygelzimer, Francesco Orabona, Chicheng Zhang
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our algorithm experimentally, showing that it also performs favorably against earlier algorithms. |
| Researcher Affiliation | Collaboration | 1Yahoo Research, New York, NY 2Stony Brook University, Stony Brook, NY 3University of California, San Diego, La Jolla, CA. |
| Pseudocode | Yes | Algorithm 1 Second Order Banditron Algorithm (SOBA) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We used three different datasets from Kakade et al. (2008): Syn Sep, Syn Non Sep, Reuters4. The first two are synthetic, with 10^6 samples in R^400 and 9 classes. [...] We also report the performance on Covtype from Lib SVM repository. |
| Dataset Splits | No | The paper mentions datasets used but does not provide specific training/validation/test split percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms implemented but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | In the experiments, we only changed the exploration rate γ, leaving fixed all the other parameters the algorithms might have. In particular, for the PNewtron we set α = 10, β = 0.01, and D = 1, as in Hazan & Kale (2011). In SOBA, a is fixed to 1 in all the experiments. |