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..
Learning the Learning Rate for Prediction with Expert Advice
Authors: Wouter M. Koolen, Tim van Erven, Peter Grünwald
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Appendix A we describe the data used to generate Figure 1 and explain why the regret obtained by LLR is significantly smaller than the regret of Ada Hedge, FTL and all other tunings described above. |
| Researcher Affiliation | Academia | Wouter M. Koolen Queensland University of Technology and UC Berkeley EMAIL Tim van Erven Leiden University, the Netherlands EMAIL Peter D. Gr unwald Leiden University and Centrum Wiskunde & Informatica, the Netherlands EMAIL |
| Pseudocode | Yes | Algorithm 1 LLR(πah, π ). The grid η1, η2, . . . and weights π1, π2, . . . are defined in (8) and (12). |
| Open Source Code | No | The paper does not provide any links to open-source code or explicit statements about code availability. |
| Open Datasets | No | The paper uses 'Example data (details in Appendix A)' which describes how data was generated for illustrative purposes, but it does not refer to a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper describes generating example data for Figure 1, but does not mention specific training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the LLR algorithm itself, but it does not specify concrete hyperparameter values or system-level training settings for an experimental setup. |