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 [1].
Contextual Multinomial Logit Bandits with General Value Functions
Authors: Mengxiao Zhang, Haipeng Luo
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we consider contextual MNL bandits with a general value function class that contains the ground truth, borrowing ideas from a recent trend of studies on contextual bandits. Specifically, we consider both the stochastic and the adversarial settings, and propose a suite of algorithms, each with different computation-regret trade-off. ... Throughout the paper, we use two running examples to illustrate the concrete regret bounds our different algorithms achieve: the finite class and the linear class. In particular, for the linear class, this leads to five new results, summarized in Table 1 together with previous results. |
| Researcher Affiliation | Academia | Mengxiao Zhang University of Iowa EMAIL Haipeng Luo University of Southern California EMAIL |
| Pseudocode | Yes | Algorithm 1 Contextual MNL Algorithms with an Offline Regression Oracle, Algorithm 2 Contextual MNL Algorithms via an Online Regression Oracle, Algorithm 3 Feel-Good Thompson Sampling for Contextual MNL bandits |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is purely theoretical and does not conduct experiments using publicly available datasets. It refers to 'finite class' and 'linear class' as abstract function classes for theoretical analysis, not concrete datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve the use of datasets with training, validation, or test splits for empirical evaluation. |
| Hardware Specification | No | This paper is purely theoretical and does not describe any specific hardware (e.g., GPU models, CPU types, memory) used for running experiments. |
| Software Dependencies | No | This paper is purely theoretical and does not mention any specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks) required to reproduce experiments. |
| Experiment Setup | No | This paper is purely theoretical and does not describe any experimental setup details, such as hyperparameter values, optimization settings, or training configurations. |