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..
Cornering Stationary and Restless Mixing Bandits with Remix-UCB
Authors: Julien Audiffren, Liva Ralaivola
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a regret analysis for this bandit strategy; two noticeable features of Remix-UCB are that i) it reduces to the regular Improved-UCB when the ϕ-mixing coefficients are all 0, i.e. when the i.i.d scenario is recovered, and ii) when ϕ(n) = O(n α), it is able to ensure a controlled regret of order eΘ (α 2)/α log1/α T , where encodes the distance between the best arm and the best suboptimal arm, even in the case when α < 1, i.e. the case when the ϕ-mixing coefficients are not summable. |
| Researcher Affiliation | Academia | Julien Audiffren CMLA ENS Cachan, Paris Saclay University 94235 Cachan France EMAIL Liva Ralaivola QARMA, LIF, CNRS Aix Marseille University F-13289 Marseille cedex 9, France EMAIL |
| Pseudocode | Yes | Algorithm 1 Remix-UCB, with parameter K, (αi)i=1 K, T, G defined in (11) |
| Open Source Code | No | The paper does not provide a statement about making its source code available or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not mention using or providing access to any publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not specify any training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup including hyperparameters or system-level training settings. |