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..
New Results for Random Walk Learning
Authors: Jeffrey C. Jackson, Karl Wimmer
JMLR 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a new approach to weak parity learning that leads to quasi-efficient uniform random walk learnability of TOP. We also introduce a more general random walk model and give two positive results in this new model: DNF is efficiently learnable and juntas are efficiently agnostically learnable. |
| Researcher Affiliation | Academia | Jeffrey C. Jackson EMAIL Karl Wimmer EMAIL Duquesne University 600 Forbes Avenue Pittsburgh, PA 15282-1754 |
| Pseudocode | Yes | Algorithm 1: PT |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper discusses theoretical models and algorithms (e.g., uniform random walk model, product distributions) but does not use or refer to any specific publicly available datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, therefore, it does not specify any dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical algorithm design and analysis, and therefore does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithm design, without detailing any specific software dependencies or version numbers for implementation. |
| Experiment Setup | No | The paper presents theoretical algorithms and complexity analysis without describing any practical experimental setup, hyperparameter values, or training configurations. |