Adversarial Combinatorial Bandits with General Non-linear Reward Functions
Authors: Yanjun Han, Yining Wang, Xi Chen
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we study the adversarial combinatorial bandit with a known non-linear reward function, extending existing work on adversarial linear combinatorial bandit. ... We show that, with N arms and subsets of K arms being chosen at each of T time periods, the minimax optimal regret is eΘd(...) if the reward function is a d-degree polynomial with d < K, and ΘK(...) if the reward function is not a low-degree polynomial. ... The following theorem is a rigorous statement of the main result of this paper. Theorem 1. ... In the rest of this section we sketch the proofs of Theorem 1 by studying the upper bounds and lower bounds separately. |
| Researcher Affiliation | Academia | 1Stern School of Business, New York University, New York, NY 10012, USA 2Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA 3Warrington College of Business, University of Florida, Gainesville, FL 32611, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | As a theoretical paper, it does not involve training models on datasets, and thus does not provide information on publicly available datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical validation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper that does not describe an experimental setup or hyperparameter details. |