Multi-task Representation Learning for Pure Exploration in Bilinear Bandits
Authors: Subhojyoti Mukherjee, Qiaomin Xie, Josiah Hanna, Robert Nowak
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct proof-of-concept experiments on both single and multi-task bilinear bandits. In the single-task experiment, we compare against the state-of-the-art RAGE algorithm (Fiez et al., 2019). We show in Figure 1 (left) that GOBLIN requires fewer samples than the RAGE with an increasing number of arms. In the multi-task experiment, we compare against the state-of-the-art Dou Exp Des algorithm (Du et al., 2023). We show in Figure 1 (right) that GOBLIN requires fewer samples than Dou Exp Des with an increasing number of tasks. As experiments are not a central contribution, we defer a fuller description of the experimental set-up to Appendix A.8. |
| Researcher Affiliation | Academia | Subhojyoti Mukherjee ECE Department UW-Madison Wisconsin, Madison smukherjee27@wisc.edu Qiaomin Xie ISy E Department UW-Madison Wisconsin, Madison Josiah P. Hanna CS Department UW-Madison Wisconsin, Madison Robert Nowak ECE Department UW-Madison Wisconsin, Madison |
| Pseudocode | Yes | Algorithm 1 G-Optimal Design for Bilinear Bandits (GOBLIN) for single-task setting... Algorithm 2 G-Optimal Design for Bilinear Bandits (GOBLIN) for multi-task setting |
| Open Source Code | No | No statement or link providing access to the source code for the described methodology was found. |
| Open Datasets | No | The paper does not provide information about specific datasets used in the experiments nor their public availability. It only refers to a 'number of arms' and 'number of tasks' in its experimental section. |
| Dataset Splits | No | The paper mentions experiments but does not provide details on dataset splits (training, validation, test) or cross-validation setup. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) were mentioned in the paper. |
| Experiment Setup | No | As experiments are not a central contribution, we defer a fuller description of the experimental set-up to Appendix A.8. The main body of the paper does not contain specific hyperparameters or training details. |