Adversarial Blocking Bandits
Authors: Nicholas Bishop, Hau Chan, Debmalya Mandal, Long Tran-Thanh
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our focus and results are largely theoretical. In particular, our contributions advance our understanding of multi-armed bandit models and its theoretical limitations and benefit the general (theoretical) machine learning community, specifically the multi-armed bandit and online learning communities. |
| Researcher Affiliation | Academia | Nicholas Bishop University of Southampton, UK nb8g13@soton.ac.uk; Hau Chan University of Nebraska-Lincoln, USA hchan3@unl.edu; Debmalya Mandal Columbia University, USA dm3557@columbia.edu; Long Tran-Thanh University of Warwick, UK long.tran-thanh@warwick.ac.uk |
| Pseudocode | Yes | Algorithm 1: Greedy-BAA; Algorithm 2: Repeating Greedy Algorithm (RGA); Algorithm 3: Meta Repeating Greedy Algorithm (META-RGA) |
| Open Source Code | No | The paper does not contain any statements about making its source code publicly available, nor does it provide any links to a code repository. |
| Open Datasets | No | This paper is theoretical and does not involve empirical training on specific datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical validation on specific datasets. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments that would require specific hardware, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithm design. It does not mention any specific software dependencies with version numbers for implementation or analysis. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore it does not provide details on experimental setup, hyperparameters, or training settings. |