Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
Authors: Runzhe Wan, Lin Ge, Rui Song
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed method is further supported by extensive experiments. ... The Bayes regret for Gaussian bandits clearly demonstrates the benefits of information sharing with our algorithm. The proposed method is further supported by extensive experiments. ... 6 Experiments 6.1 Synthetic Experiments 6.2 Movie Lens Experiments |
| Researcher Affiliation | Academia | Runzhe Wan Lin Ge Rui Song Department of Statistics North Carolina State University {rwan, lge, rsong}@ncsu.edu |
| Pseudocode | Yes | Algorithm 1: MTTS: Multi-task Thompson Sampling ... Algorithm 2: Computationally Efficient Variant of MTTS under the Sequential Setting |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the empirical performance on the Movie Lens 1M dataset [24] |
| Dataset Splits | No | The paper does not specify distinct training, validation, and test dataset splits with explicit percentages, sample counts, or citations to predefined splits for the Movie Lens dataset, nor does it explicitly mention a validation set. For synthetic experiments, data is generated, not split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions implementing methods but does not provide specific software names with version numbers (e.g., Python, PyTorch, TensorFlow versions or specific library versions) for reproducibility. |
| Experiment Setup | Yes | In the main text, we present results with N = 200, T = 200, K = 8 and d = 15. We set φ(xi, a) = (1T a , φT i,a)T , where 1a is a length-K indicator vector taking value 1 at the a-th entry, and φi,a is sampled from N(0, Id K). The coefficient vector θ is sampled from N(0, d 1Id). For Gaussian bandits, we set σ = 1 and Σ = σ2 1IK, for different values of σ1. For Bernoulli bandits, we vary the precision ψ. |