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..
Fast rates for prediction with limited expert advice
Authors: El Mehdi Saad, Gilles Blanchard
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the problem of minimizing the excess generalization error... we show that if we are allowed to see the advice of only one expert per round... the worst-case excess risk is Ω(1/T) ... However, if we are allowed to see at least two actively chosen expert advices per training round... the fast rate O(1/T) can be achieved. We design novel algorithms achieving this rate in this setting, and in the setting where the learner has a budget constraint on the total number of observed expert advices, and give precise instance-dependent bounds on the number of training rounds and queries needed to achieve a given generalization error precision. |
| Researcher Affiliation | Academia | El Mehdi Saad1, Gilles Blanchard1,2 1Laboratoire de Mathématiques d Orsay, CNRS, Université Paris-Saclay; 2Inria |
| Pseudocode | Yes | Algorithm 1 Budgeted aggregation; Algorithm 2 Two-point feedback |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes theoretical frameworks and algorithms but does not mention the use of any specific dataset for training, nor its public availability. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, so it does not describe training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for computations or experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers required to reproduce the work. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore no specific experimental setup details such as hyperparameters or training configurations are provided. |