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 Closer Look at Adaptive Regret
Authors: Dmitry Adamskiy, Wouter M. Koolen, Alexey Chernov, Vladimir Vovk
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We compute the exact worst-case adaptive regret of Fixed Share. We re-derive the tracking regret bounds from these adaptive regret bounds, showing that the latter are in fact more fundamental. We prove that Fixed Share is optimal for adaptive regret: the worst-case adaptive regret of any algorithm is at least that of an instance of Fixed Share. |
| Researcher Affiliation | Academia | Dmitry Adamskiy EMAIL Computer Learning Research Centre and Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK; Wouter M. Koolen EMAIL Centrum Wiskunde & Informatica Science Park 123, 1098XG Amsterdam, The Netherlands; Alexey Chernov EMAIL School of Computing, Engineering and Mathematics, University of Brighton Moulsecoomb, Brighton, BN2 4GJ, UK; Vladimir Vovk EMAIL Computer Learning Research Centre and Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK |
| Pseudocode | Yes | Algorithm 1 Adaptive Aggregating Algorithm Input: Prior nonnegative weights p(t), t = 1, 2, . . . , with p(1) > 0 vn 1 := p(1), n = 1, . . . , N for t = 1, 2, . . . do Play weights un t := vn t PN j=1 vj t Read the experts losses ℓn t , n = 1, . . . , N Set vn t+1 := p(t + 1) + vn t e ℓn t PN j=1 uj te ℓj t , n = 1, . . . , N |
| Open Source Code | No | The paper does not provide any statement about releasing code, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using specific datasets. Therefore, there is no mention of publicly available datasets or access information. |
| Dataset Splits | No | As the paper describes theoretical work and does not perform experiments on specific datasets, there is no discussion of dataset splits. |
| Hardware Specification | No | The paper describes theoretical research and does not detail any experimental setup requiring specific hardware. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and analysis, without detailing implementation or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical, presenting algorithm analysis and bounds rather than experimental results with specific setup details or hyperparameters. |