Adapting to Delays and Data in Adversarial Multi-Armed Bandits
Authors: Andras Gyorgy, Pooria Joulani
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze variants of the Exp3 algorithm that tune their step size using only information (about the losses and delays) available at the time of the decisions, and obtain regret guarantees that adapt to the observed (rather than the worst-case) sequences of delays and/or losses. First, through a remarkably simple proof technique, we show that with proper tuning of the step size, the algorithm achieves an optimal (up to logarithmic factors) regret of order p log(K)(TK + D) both in expectation and in high probability |
| Researcher Affiliation | Industry | 1Deep Mind, London, UK. Correspondence to: Andr as Gy orgy <agyorgy@deepmind.com>, Pooria Joulani <pjoulani@deepmind.com>. |
| Pseudocode | Yes | Algorithm 1: Delay-Adaptive Exp3 (DAda-Exp3). |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not involve experimental training on a dataset. |
| Dataset Splits | No | This paper is theoretical and does not involve dataset splits for training/validation/test. |
| Hardware Specification | No | The paper is theoretical and does not describe hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details like hyperparameters or training settings. |