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..
Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment
Authors: Govinda Kamath, Tavor Baharav, Ilan Shomorony
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In order to validate the Adaptive Spectral Top-k algorithm, we conducted two types of experiments: (1) controlled experiments on simulated data for a crowdsourcing model with symmetric errors; (2) pairwise sequence alignment experiments on real DNA sequencing data. |
| Researcher Affiliation | Collaboration | 1Microsoft Research New England, Cambridge, MA 2Department of Electrical Engineering, Stanford University, Stanford, CA 3Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, IL EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Spectral estimation of u |
| Open Source Code | Yes | Our code is publicly available online at github.com/Tavor B/adaptive Spectral. |
| Open Datasets | Yes | Using the Pac Bio E. coli data set [38]... We also consider the NCTC4174 dataset of [39]... [38] Pacific Biosciences Inc. Pacbio e. coli dataset, 2013. URL https://github.com/ Pacific Biosciences/Dev Net/wiki/E.-coli-Bacterial-Assembly. [39] J. Parkhill et al. National collection of type cultures (NCTC)3000. URL https://www. sanger.ac.uk/resources/downloads/bacteria/nctc/. |
| Dataset Splits | No | The paper uses datasets but does not explicitly provide specific training, validation, or test dataset splits (e.g., percentages or sample counts) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions running Algorithm 2 with 'some slight modifications' and uses 'various budgets' but does not provide specific experimental setup details such as concrete hyperparameter values or training configurations. |