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].
Optimal Algorithms for Learning Partitions with Faulty Oracles
Authors: Adela DePavia, Olga Medrano Martin del Campo, Erasmo Tani
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design algorithms for this task and prove that they achieve optimal query complexity. |
| Researcher Affiliation | Academia | Adela Frances De Pavia University of Chicago EMAIL Olga Medrano Martín del Campo University of Chicago EMAIL Erasmo Tani University of Chicago EMAIL |
| Pseudocode | Yes | Algorithm 1: Learn(V, α, k, ℓyes) |
| Open Source Code | No | The paper is theoretical and does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experiments or datasets, thus no information about training data availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments or datasets, thus no information about validation splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not involve empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs rather than empirical experiments, so no experimental setup details are provided. |