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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Authors: Tor Lattimore
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main contribution is the new assumption, algorithm, and the proof of Theorem 2 (see 2). The upper bound is also complemented by an asymptotic lower bound ( 3) that applies to all strategies with sub-polynomial regret and all bandit problems with bounded kurtosis. |
| Researcher Affiliation | Industry | Tor Lattimore EMAIL Now at Deep Mind, London. |
| Pseudocode | No | The paper describes an algorithm in prose within Section 2, but it is not presented in a structured pseudocode block or a clearly labeled algorithm figure. |
| Open Source Code | No | The paper does not provide any concrete access to source code (e.g., a specific repository link, an explicit code release statement, or mention of code in supplementary materials) for the methodology described. |
| Open Datasets | No | This is a theoretical paper focusing on mathematical derivations and algorithm design. It does not conduct empirical studies with datasets; hence, there is no mention of publicly available datasets for training or other purposes. |
| Dataset Splits | No | This is a theoretical paper and does not describe empirical experiments. Therefore, there are no specific dataset split details (e.g., train/validation/test percentages or counts) provided. |
| Hardware Specification | No | This is a theoretical paper. It does not describe any experimental setup that would require hardware, and thus no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper focusing on mathematical and algorithmic contributions. It does not describe any experimental setup that would require specific software dependencies or their versions. |
| Experiment Setup | No | This is a theoretical paper. It does not describe any empirical experimental setup, and therefore no specific hyperparameters, training configurations, or system-level settings are provided. |