Multinomial Logit Bandit with Linear Utility Functions
Authors: Mingdong Ou, Nan Li, Shenghuo Zhu, Rong Jin
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate LUMB on synthetic data and compare it to three existing alternative algorithms. We demonstrate the superiority of LUMB on cumulative regret. Moreover, we show that the estimated linear parameters of utility function and utilities will asymptotically converge to the real value. |
| Researcher Affiliation | Industry | Mingdong Ou, Nan Li, Shenghuo Zhu, Rong Jin, Alibaba Group, Hang Zhou, China {mingdong.omd, nanli.ln, shenghuo.zhu, jinrong.jr}@alibaba-inc.com |
| Pseudocode | Yes | Algorithm 1 Linear Utility MNL-Bandit |
| Open Source Code | No | The paper does not provide any statement about making the source code available or include a link to a code repository. |
| Open Datasets | No | The synthetic data is generated randomly. N rewards are sampled from interval (0, 1] uniformly. d-dimension parameter vector of utility function, θ , is sampled from [0, 1]d uniformly, then is normalized to 1. N d-dimension feature vectors are sampled from [0, 1]d uniformly. |
| Dataset Splits | No | The paper does not specify any training, validation, or test dataset splits. It mentions 'synthetic data sets' that are randomly generated for experiments, but no partitioning details are provided. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | We conduct empirical experiments on synthetic data sets with N = 1000,d = 10. Subset size K is set to 10. Experiments are all performed on ten randomly generated data sets and the results show below are all average of results on these data sets. |