Optimal Algorithms for Learning Partitions with Faulty Oracles
Authors: Adela DePavia, Olga Medrano Martin del Campo, Erasmo Tani
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 adepavia@uchicago.edu Olga Medrano Martín del Campo University of Chicago omedranomdelc@uchicago.edu Erasmo Tani University of Chicago etani@uchicago.edu |
| 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. |