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.
Multi-Task Learning for Contextual Bandits
Authors: Aniket Anand Deshmukh, Urun Dogan, Clay Scott
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 14,044 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,156 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 on synthetic data and some multi-class classification datasets. We conclude in Section 6. |
| Researcher Affiliation | Collaboration | Aniket Anand Deshmukh Department of EECS University of Michigan Ann Arbor Ann Arbor, MI 48105 EMAIL Urun Dogan Microsoft Research Cambridge CB1 2FB, UK EMAIL |
| Pseudocode | Yes | Algorithm 1 KMTL-UCB |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We test our algorithm on synthetic data and some multi-class classification datasets. In the case of multi-class datasets, the number of arms N is the number of classes and the reward is 1 if we predict the correct class, otherwise it is 0. We consider the following datasets: Digits (N = 10, d = 64), Letter (N = 26, d = 16), MNIST (N = 10, d = 780 ), Pendigits (N = 10, d = 16), Segment (N = 7, d = 19) and USPS (N = 10, d = 256). |
| Dataset Splits | Yes | We separate the data into two parts validation set and test set. We use all Gaussian kernels and pre-select the bandwidth of kernels using five fold cross-validation on a holdout validation set and we use β = 0.1 for all experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We use all Gaussian kernels and pre-select the bandwidth of kernels using five fold cross-validation on a holdout validation set and we use β = 0.1 for all experiments. |