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].
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
Authors: Mingrui Zhang, Lin Chen, Hamed Hassani, Amin Karbasi
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose three online algorithms for submodular maximization. The ๏ฌrst one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from T 1/2 [18] and T 3/2 [17] to 1, and achieves a (1 1/e)-regret bound of O(T 4/5). The second one, Bandit-Frank-Wolfe, is the ๏ฌrst bandit algorithm for continuous DR-submodular maximization, which achieves a (1 1/e)regret bound of O(T 8/9). Finally, we extend Bandit-Frank-Wolfe to a bandit algorithm for discrete submodular maximization, Responsive-Frank-Wolfe, which attains a (1 1/e)-regret bound of O(T 8/9) in the responsive bandit setting. |
| Researcher Affiliation | Academia | Department of Statistics and Data Science, Yale University Department of Electrical Engineering, Yale University Department of Electrical and Systems Engineering, University of Pennsylvania Department of Computer Science, Yale University EMAIL EMAIL |
| Pseudocode | Yes | We present our proposed Mono-Frank-Wolfe algorithm in Algorithm 1. We describe our algorithm formally in Algorithm 2. We present Responsive-Frank-Wolfe in Algorithm 3. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies with datasets, therefore no public dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with data, so no training/validation/test dataset splits are discussed. |
| Hardware Specification | No | The paper is theoretical and does not include details on hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about specific experimental setup, hyperparameters, or system-level training settings. |