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..
Luckiness in Multiscale Online Learning
Authors: Wouter M. Koolen, Muriel F. Pérez-Ortiz
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate experimentally the superior performance of our scale-adaptive algorithm and discuss the subtle relationship of our results to Freund s 2016 open problem. (Abstract) and Section 6 Experiments on Synthetic Data |
| Researcher Affiliation | Academia | Muriel Felipe Pérez-Ortiz Centrum Wiskunde & Informatica (CWI) EMAIL Wouter M. Koolen CWI and University of Twente EMAIL |
| Pseudocode | Yes | Figure 1: MUSCADA and Figure 3: Optimistic MUSCADA, given as update w.r.t. Figure 1. |
| Open Source Code | Yes | Generating this figure with the code in the supplementary material takes 3 seconds on an Intel i7-7700 processor. |
| Open Datasets | No | The paper uses 'Synthetic Data' which they describe how to generate themselves, but does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating synthetic data for experiments but does not provide specific train/validation/test dataset splits (percentages or counts). |
| Hardware Specification | Yes | Generating this figure with the code in the supplementary material takes 3 seconds on an Intel i7-7700 processor. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | Parameters: A vector uk > 0 of initial weights, initial strictly positive learning rates η0,k 1/(2σk), and real, continuous nonincreasing functions Hk : R+ 7 R with Hk(0) = 1. ... We take K = 50 experts and set σk = 1/k for each k K. ... For the hard case, we set λk = 0 for all k. For the lucky case, we set λ2 = 1/5 instead. |