Multi-Task Learning for Contextual Bandits
Authors: Aniket Anand Deshmukh, Urun Dogan, Clay Scott
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 aniketde@umich.edu Urun Dogan Microsoft Research Cambridge CB1 2FB, UK urun.dogan@skype.net |
| 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. |