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..
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Authors: Alina Beygelzimer, David Pal, Balazs Szorenyi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide an empirical evaluation on our algorithms, verifying their effectiveness on linearly separable datasets. We generated strongly and weakly linearly separable datasets with K = 3 classes in R3 i.i.d. from two data distributions. Figures 3a and 3b show visualizations of the two datasets, along with detailed descriptions of the distributions. |
| Researcher Affiliation | Collaboration | 1Yahoo Research, New York, NY, USA 2New York University, New York, NY, USA 3University of Southern California, Los Angeles, CA, USA 4Microsoft Research, New York, NY, USA. |
| Pseudocode | Yes | Algorithm 1 BANDIT ALGORITHM FOR STRONGLY LINEARLY SEPARABLE EXAMPLES...Algorithm 2 KERNELIZED BANDIT ALGORITHM |
| Open Source Code | No | The paper mentions a MATLAB toolbox by Orabona (2009) as being available, but this is an external tool and not the open-source code for the specific algorithms and methods presented in this paper. |
| Open Datasets | No | We generated strongly and weakly linearly separable datasets with K = 3 classes in R3 i.i.d. from two data distributions. Figures 3a and 3b show visualizations of the two datasets, along with detailed descriptions of the distributions. |
| Dataset Splits | No | The paper states details about the generated datasets, such as the number of classes, dimensionality, and class distribution (e.g., "80% of the examples belong to class 1, 10% belong to class 2 and 10% belong to class 3."). However, it does not specify any train, validation, or test splits, nor does it mention cross-validation or other explicit data partitioning methodologies required for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments, such as CPU or GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions implementing algorithms and using the BANDITRON algorithm by Orabona (2009), which is a MATLAB toolbox. However, it does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, specific library versions) that would be needed to reproduce the experiments. |
| Experiment Setup | Yes | BANDITRON has an exploration rate parameter, for which we tried values 0.02, 0.01, 0.005, 0.002, 0.001, 0.0005. |