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..
Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts
Authors: Dirk Van Der Hoeven, Ciara Pike-Burke, Hao Qiu, Nicolò Cesa-Bianchi
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithm is empirically evaluated on synthetic data. ... Our experiments use two sets of productivity functions defined on a uniform (random) grid C of N payments on [1/2, 1]. |
| Researcher Affiliation | Academia | 1Korteweg-de Vries Institute for Mathematics University of Amsterdam, Amsterdam, The Netherlands 2Department of Mathematics, Imperial College London, London, UK 3Universit a degli Studi di Milano, Milan, Italy 4Politecnico di Milano, Milan, Italy. |
| Pseudocode | Yes | Algorithm 1 LCB-GAPTRON |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | Our algorithm is empirically evaluated on synthetic data. ... We sampled N numbers c1, . . . , c N uniformly at random from [1/2, 1] and defined pj(ci) = ci for all i [N] and all j [K]. |
| Dataset Splits | No | The paper uses synthetic data for experiments but does not explicitly describe train, validation, or test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | Figure 1. Cumulative cost over time for the choice of parameters K = 5, N = 5, T = 10^4, λ = 10^-2. ... Figure 2. Total cost for the following choices of parameters: K = 10, N = 10, T = 10^5, λ = 10^-2 (top plot), K = 10, N = 10, T = 10^5, λ = 10^-3 (central plot), K = 20, N = 50, T = 10^5, λ = 10^-2 (bottom plot). ... The error bars show the standard deviation of the cost averaged over 20 repetitions. |