Comparator-Adaptive Convex Bandits
Authors: Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our contribution is primarily theoretical, and we do not foresee any negative ethical or societal impact.Our results are summarized in Table 1. Regret is measured with respect to the total loss of an arbitrary point u Rd in the unconstrained setting, or an arbitrary point u W in the constrained setting with a decision space W contained in the unit ball. T is the total number of rounds, 1/c is radius of the largest ball contained by W, and ν is the self-concordant parameter. Both c and ν are bounded by O(d). |
| Researcher Affiliation | Academia | Dirk van der Hoeven Mathematical Institute Leiden University dirk@dirkvanderhoeven.com Ashok Cutkosky Boston University ashok@cutkosky.com Haipeng Luo Computer Science Department University of Southern California haipengl@usc.edu |
| Pseudocode | Yes | Algorithm 1 Black-Box Reduction with Full Information, Algorithm 2 Black-Box Reduction for Linear Bandits, Algorithm 5 Black-Box Comparator-Adaptive Convex Bandit Algorithm |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper focuses on theoretical algorithm development and regret analysis, not on empirical evaluation with datasets. Therefore, it does not mention publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and regret analysis, not on empirical experiments. Therefore, it does not provide hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and regret analysis, not on empirical experiments. Therefore, it does not provide details about an experimental setup, hyperparameters, or training configurations. |