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 [1].
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. |