Prediction with Limited Advice and Multiarmed Bandits with Paid Observations
Authors: Yevgeny Seldin, Peter Bartlett, Koby Crammer, Yasin Abbasi-Yadkori
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an algorithm that achieves N M T ln N regret on T rounds of this game. ... We present an algorithm that achieves O (c N ln N)1/3 T 2/3 + T ln N regret on T rounds of this game in the worst case. Furthermore, we present a number of reļ¬nements that treat armand time-dependent observation costs and achieve lower regret under benign conditions. We present lower bounds that show that, apart from the logarithmic factors, the worst-case regret bounds cannot be improved. More illuminating proofs are provided in Section 3, whereas more technical results are provided in the appendix. |
| Researcher Affiliation | Academia | Yevgeny Seldin YEVGENY.SELDIN@GMAIL.COM Queensland University of Technology and UC Berkeley Peter Bartlett BARTLETT@EECS.BERKELEY.EDU UC Berkeley and Queensland University of Technology Koby Crammer KOBY@EE.TECHNION.AC.IL The Technion Yasin Abbasi-Yadkori YASIN.ABBASI@GMAIL.COM Queensland University of Technology and UC Berkeley |
| Pseudocode | Yes | Algorithm 1 Prediction with limited advice. Algorithm 2 Multiarmed Bandits with Paid Observations. |
| Open Source Code | No | The paper does not contain any statements about releasing source code, nor does it provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on datasets, so there is no mention of public or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data, thus no dataset splits for training, validation, or testing are mentioned. |
| Hardware Specification | No | The paper focuses on theoretical contributions (algorithms, proofs, bounds) and does not describe any empirical experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not discuss implementation details or empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and presents algorithms and their theoretical bounds, without describing any empirical experimental setups, hyperparameter values, or system-level training settings. |