Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-task Representation Learning for Pure Exploration in Bilinear Bandits
Authors: Subhojyoti Mukherjee, Qiaomin Xie, Josiah Hanna, Robert Nowak
NeurIPS 2023 | Venue PDF | 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 EMAIL 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. |