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 | Conference PDF | Archive PDF | Plain Text | 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. |