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.
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
Authors: Jiabin Lin, Shana Moothedath, Namrata Vaswani
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 31,367 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,632 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 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. |