Necessarily Optimal One-Sided Matchings

Authors: Hadi Hosseini, Vijay Menon, Nisarg Shah, Sujoy Sikdar5481-5488

AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We design efficient algorithms to check if a given matching is NPO or NRM, and to check whether such a matching exists given top-k partial preferences. We also study online algorithms for eliciting partial preferences adaptively, and prove bounds on their competitive ratio.
Researcher Affiliation Academia 1 College of Information Sciences and Technology, Penn State University 2 David R. Cheriton School of Computer Science, University of Waterloo 3 Department of Computer Science, University of Toronto 4 Department of Computer Science, Binghamton University
Pseudocode Yes Algorithm 1: A 2( n+1)-competitive elicitation algorithm; Algorithm 2: Algorithm to compute sigopt P (S, T, F) and arg sigopt P (S, T, F); Algorithm 3: Algorithm to check if a given matching is NRM given a top-k preference profile; Algorithm 4: Algorithm to check if an NRM matching exists given a top-k preference profile.
Open Source Code No The paper does not provide a direct link to source code or explicitly state that the code for the methodology is being released.
Open Datasets No This is a theoretical paper focusing on algorithm design and proofs; it does not involve training models on datasets.
Dataset Splits No This is a theoretical paper, and thus does not involve validation datasets or splits.
Hardware Specification No This is a theoretical paper, and therefore does not specify hardware used for experiments.
Software Dependencies No This is a theoretical paper and does not mention specific software dependencies with version numbers for experimental reproducibility.
Experiment Setup No This is a theoretical paper and does not describe an experimental setup with hyperparameters or training settings.