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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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. |