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..
Bandit Learning with Delayed Impact of Actions
Authors: Wei Tang, Chien-Ju Ho, Yang Liu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose an algorithm that achieves a regret of O(KT 2/3) and show a matching regret lower bound of (KT 2/3), where K is the number of arms and T is the learning horizon. Our results complement the bandit literature by adding techniques to deal with actions with long-term impacts and have implications in designing fair algorithms. Finally, we conduct a series of simulations showing that our algorithms compare favorably to other state-of-the-art methods proposed in other application domains. |
| Researcher Affiliation | Academia | Wei Tang , Chien-Ju Ho , and Yang Liu Washington University in St. Louis, University of California, Santa Cruz EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Action-Dependent UCB; Algorithm 2 Reduction Template; Algorithm 3 History-Dependent UCB |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of its source code. |
| Open Datasets | No | The paper refers to 'simulations' but does not specify any publicly available datasets used, nor does it provide any concrete access information for data. |
| Dataset Splits | No | The paper mentions 'simulations' but does not provide specific dataset split information (e.g., percentages, sample counts, or cross-validation details) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper mentions 'simulations' but does not provide specific hardware details such as exact GPU/CPU models or processor types used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper states 'The detailed setups and discussion are in Appendix I' but Appendix I is not provided. The main text does not contain specific experimental setup details such as hyperparameter values or training configurations. |