Optimal Weak to Strong Learning
Authors: Kasper Green Larsen, Martin Ritzert
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is purely theoretic (set within learning theory) and is thus not expected to impact society outside of the scientific community |
| Researcher Affiliation | Academia | Kasper Green Larsen Department of Computer Science Aarhus University Aarhus, Denmark larsen@cs.au.dk Martin Ritzert Department of Computer Science Aarhus University Aarhus, Denmark ritzert@cs.au.dk |
| Pseudocode | Yes | Algorithm 1: Sub-Sample(A, B) Algorithm 2: Optimal weak-to-strong learner |
| Open Source Code | No | (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not describe using any dataset for training. The Ethics Review Checklist states 'N/A' for questions related to experiments and data. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental validation using dataset splits. The Ethics Review Checklist states 'N/A' for questions related to experiments and data. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental hardware. The Ethics Review Checklist states 'N/A' for 'total amount of compute and the type of resources used'. |
| Software Dependencies | No | The paper is purely theoretical and does not describe any software dependencies with specific version numbers for experimental reproducibility. The Ethics Review Checklist states 'N/A' for 'include the code'. |
| Experiment Setup | No | The paper is purely theoretical and does not describe an experimental setup with hyperparameters or training details. The Ethics Review Checklist states 'N/A' for 'specify all the training details'. |