Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Multinomial Logit Bandit with Linear Utility Functions
Authors: Mingdong Ou, Nan Li, Shenghuo Zhu, Rong Jin
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,699 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,376 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |