Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret
Authors: Alina Beygelzimer, Francesco Orabona, Chicheng Zhang
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 17,457 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,326 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |