Clustering with Non-adaptive Subset Queries

Authors: Hadley Black, Euiwoong Lee, Arya Mazumdar, Barna Saha

NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical Our results are all purely theoretical and are expressed in the form of theorem statements, in which any assumptions are explicitly stated.
Researcher Affiliation Academia Hadley Black UC San Diego Euiwoong Lee University of Michigan Arya Mazumdar UC San Diego UC San Diego
Pseudocode Yes Algorithm 1: Non-adaptive Algorithm for Constant k; Algorithm 2: Non-adaptive Algorithm for General k; Algorithm 3: Sample-Based Algorithm Using Bounded Queries; Algorithm 4: Algorithm for the B-Balanced Case; Algorithm 5: Second Algorithm for the B-Balanced Case; Algorithm 6: Deterministic 2-Round Algorithm; Algorithm 7: Two Round Algorithm for Balanced Clustering; Algorithm 8: Sample-Based Algorithm Using Unbounded Queries
Open Source Code No The paper states: 'Our paper does not include any experiments.' It does not contain any explicit statement about providing open-source code for the described methodology or a link to a code repository.
Open Datasets No The paper states: 'Our paper does not include any experiments.' Therefore, no datasets are used for training.
Dataset Splits No The paper states: 'Our paper does not include any experiments.' Therefore, no validation splits are provided.
Hardware Specification No The paper states: 'Our paper does not include any experiments.' As a theoretical paper, it does not describe hardware used for experiments.
Software Dependencies No The paper states: 'Our paper does not include any experiments.' As a theoretical paper, it does not list software dependencies with version numbers for experiments.
Experiment Setup No The paper states: 'Our paper does not include any experiments.' As a theoretical paper, it does not provide details on experimental setup or hyperparameters.