Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
The Pareto Frontier of model selection for general Contextual Bandits
Authors: Teodor Vanislavov Marinov, Julian Zimmert
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a Pareto frontier of upper bounds for model selection in contextual bandits with ο¬nite sized policy classes. P.2 We present matching lower bounds that shows that our upper bounds are tight, thereby resolve the motivating open problems [Foster et al., 2020b]. P.3 We present a novel impossibility result for adapting to the number of switch points under adaptive adversaries [Besbes et al., 2014]. P.4 We negatively resolve an open problem on second order bounds for full-information [Freund, 2016]. |
| Researcher Affiliation | Collaboration | Teodor Marinov Google Research EMAIL Julian Zimmert Google Research EMAIL Author was at Johns Hopkins University during part of this work. |
| Pseudocode | Yes | Algorithm 1: Hedged FTRL |
| Open Source Code | No | The paper does not contain any statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets, training, or public dataset access. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation or specific dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training configurations. |