Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
Authors: Jiabin Lin, Shana Moothedath, Namrata Vaswani
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We presented experiments and compared the performance of our algorithm against benchmark algorithms. |
| Researcher Affiliation | Academia | 1 Department of Electrical and Computer Engineering, Iowa State University, Ames IA 50011-1250, USA. |
| Pseudocode | Yes | Algorithm 1 LRRL-Alt GDMin Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | MNIST data: We used the MNIST dataset to validate the performance of our algorithm when implemented with real-world data. |
| Dataset Splits | No | The paper describes an online learning process with epochs and sample-splitting for internal algorithm updates, but does not specify traditional train/validation/test dataset splits for model evaluation. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments are provided. |
| Software Dependencies | No | The paper states 'All experiments were conducted using Python.' but does not provide specific version numbers for Python or any other key software components. |
| Experiment Setup | Yes | We set the parameters as d = 100, and K = 5. ... We considered a noise model with a mean of 0 and a variance of 10^-6 for the bandit feedback noise. ... We ran for L = 2000 GD iterations. We considered M = 4 epochs each with 50 data samples each. |