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..
Active Learning of General Halfspaces: Label Queries vs Membership Queries
Authors: Ilias Diakonikolas, Daniel Kane, Mingchen Ma
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper focuses on theoretical analysis, presenting information-theoretic lower bounds, algorithms, and proofs (e.g., "We study the problem of learning general...", "Theorem 1.1 (Main Lower Bound)", "Theorem 1.2 (Main Algorithmic Result)", "Proof of Lemma 2.1", "Appendix G for the full proof of Theorem 1.2"). The NeurIPS checklist explicitly states: "This paper is theoretical and does not contain experiments." |
| Researcher Affiliation | Academia | Ilias Diakonikolas University of Wisconsin-Madison EMAIL Daniel M. Kane University of California, San Diego EMAIL Mingchen Ma University of Wisconsin-Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 QUERY LEARNING HALFSPACE, Algorithm 2 INITIALIZATION 1, Algorithm 3 REFINE, Algorithm 4 ANGLE TEST, Algorithm 5 INITIALIZATION 2 |
| Open Source Code | No | This paper is theoretical and does not mention releasing code, providing a link, or stating code is in supplementary material. |
| Open Datasets | No | This paper is theoretical and does not involve experiments with datasets. Therefore, it does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | This paper is theoretical and does not involve experiments or dataset evaluation. Therefore, it does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe experimental procedures that would require specific hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not describe experimental procedures that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe experimental setups, hyperparameters, or training configurations. |