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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Secretary Ranking with Minimal Inversions
Authors: Sepehr Assadi, Eric Balkanski, Renato Leme
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a matching upper and lower bound for the secretary ranking problem. We present an algorithm that ranks n elements with only O(n3/2) inversions in expectation, and show that any algorithm necessarily suffers β¦(n3/2) inversions when there are n available positions. In terms of techniques, the analysis of our algorithm draws connections to linear probing in the hashing literature, while our lower bound result relies on a general anti-concentration bound for a generic balls and bins sampling process. |
| Researcher Affiliation | Collaboration | Sepehr Assadi Rutgers University EMAIL Eric Balkanski Harvard University EMAIL Renato Paes Leme Google Research EMAIL |
| Pseudocode | Yes | ALGORITHM 1: Dense Ranking 1 Input: a set R of n positions, denoted here by [n], and at most n online arrivals. 2 for any time step t [n] and element at do 3 Deο¬ne rt := |{at | at < at and t < t}|. 4 Sample xt uniformly in the real interval [rt n t , (rt + 1) n t ] and choose erk(at) = xt . 5 Set the learned rank of at as Ο(at) = arg mini R i erk(at) and remove i from R. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper describes a theoretical problem and algorithms; it does not use or refer to any specific dataset for training or evaluation. |
| Dataset Splits | No | The paper describes a theoretical problem and algorithms; it does not involve data splitting for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup such as hyperparameters or system-level training settings. |