John Lalor

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hong yuProfessor, Director, CHORDS; Professor, Miner school of computer & information sciences, UMassAdresse e-mail validée de uml.edu
Ahmed AbbasiGiovanini Endowed Chair Professor, University of Notre DameAdresse e-mail validée de nd.edu
Pedro RodriguezResearch Scientist, FacebookAdresse e-mail validée de fb.com
Yi YangAssociate Professor, HKUST Business School, Hong Kong University of Science and TechnologyAdresse e-mail validée de ust.hk
Tsendsuren MunkhdalaiResearch Scientist, Google DeepmindAdresse e-mail validée de google.com
Amber SettleDePaul UniversityAdresse e-mail validée de cdm.depaul.edu
Nicole ForsgrenMicrosoft ResearchAdresse e-mail validée de microsoft.com
Kathleen MazorProfessor, University of Massachusetts Medical SchoolAdresse e-mail validée de umassmed.edu
Joe BarrowPattern DataAdresse e-mail validée de cs.umd.edu
Alexander HoyleAI Center Postdoctoral Fellow, ETH ZürichAdresse e-mail validée de ai.ethz.ch
Robin JiaUniversity of Southern CaliforniaAdresse e-mail validée de usc.edu
Jordan Boyd-GraberFull Professor, University of MarylandAdresse e-mail validée de umiacs.umd.edu
João SedocAssistant Professor, Department of Technology, Operations, and Statistics, New York UniversityAdresse e-mail validée de stern.nyu.edu
Weisong LiuUniversity of Massachusetts LowellAdresse e-mail validée de uml.edu
Nicholas BerenteProfessor, University of Notre DameAdresse e-mail validée de nd.edu
Beverly Park WoolfUniversity of MassachusettsAdresse e-mail validée de cs.umass.edu
William M. CampbellAmazonAdresse e-mail validée de amazon.com
Varun KumarSenior Science Manager, AWS AI LabsAdresse e-mail validée de umd.edu
Eunah ChoAmazonAdresse e-mail validée de amazon.com
HE XIEAmazonAdresse e-mail validée de amazon.com

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John Lalor

Assistant Professor, University of Notre Dame

Adresse e-mail validée de nd.edu - Page d'accueil

Natural Language Processing Machine Learning Information Systems


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Building an evaluation scale using item response theory JP Lalor, H Wu, H Yu Proceedings of the Conference on Empirical Methods in Natural Language …, 2016	95	2016
Evaluation examples are not equally informative: How should that change NLP leaderboards? P Rodriguez, J Barrow, AM Hoyle, JP Lalor, R Jia, J Boyd-Graber Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021	81	2021
Benchmarking intersectional biases in NLP JP Lalor, Y Yang, K Smith, N Forsgren, A Abbasi Proceedings of the 2022 conference of the North American chapter of the …, 2022	73	2022
Learning latent parameters without human response patterns: Item response theory with artificial crowds JP Lalor, H Wu, H Yu Proceedings of the Conference on Empirical Methods in Natural Language …, 2019	52	2019
Understanding deep learning performance through an examination of test set difficulty: A psychometric case study JP Lalor, H Wu, T Munkhdalai, H Yu Proceedings of the Conference on Empirical Methods in Natural Language …, 2018	45*	2018
Detecting hypoglycemia incidents reported in patients’ secure messages: using cost-sensitive learning and oversampling to reduce data imbalance J Chen, J Lalor, W Liu, E Druhl, E Granillo, VG Vimalananda, H Yu Journal of medical Internet research 21 (3), e11990, 2019	42	2019
Citation analysis with neural attention models T Munkhdalai, JP Lalor, H Yu Proceedings of the Seventh International Workshop on Health Text Mining and …, 2016	33	2016
Improving electronic health record note comprehension with NoteAid: randomized trial of electronic health record note comprehension interventions with crowdsourced workers JP Lalor, B Woolf, H Yu Journal of medical Internet research 21 (1), e10793, 2019	28	2019
ComprehENotes, an instrument to assess patient reading comprehension of electronic health record notes: development and validation JP Lalor, H Wu, L Chen, KM Mazor, H Yu Journal of medical Internet research 20 (4), e139, 2018	27	2018
CIFT: Crowd-informed fine-tuning to improve machine learning ability JP Lalor, H Wu, H Yu arXiv preprint arXiv:1702.08563, 2017	27*	2017
Dynamic data selection for curriculum learning via ability estimation JP Lalor, H Yu Proceedings of the Conference on Empirical Methods in Natural Language …, 2020	25	2020
Reconsidering the impact of CS1 on novice attitudes A Settle, J Lalor, T Steinbach Proceedings of the 46th ACM Technical Symposium on Computer Science …, 2015	22	2015
Efficient semi-supervised learning for natural language understanding by optimizing diversity E Cho, H Xie, JP Lalor, V Kumar, WM Campbell 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU …, 2019	21	2019
`py-irt`: A Scalable Item Response Theory Library for Python JP Lalor, P Rodriguez INFORMS Journal on Computing 35 (1), 5-13, 2023	17	2023
Constructing a psychometric testbed for fair natural language processing A Abbasi, D Dobolyi, JP Lalor, RG Netemeyer, K Smith, Y Yang Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021	16	2021
Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls C Miller, A Settle, J Lalor	16	2015
Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines JP Lalor, A Abbasi, K Oketch, Y Yang, N Forsgren ACM Transactions on Information Systems, 2024	15	2024
A computer science linked-courses learning community A Settle, J Lalor, T Steinbach Proceedings of the 2015 ACM Conference on Innovation and Technology in …, 2015	12	2015
An empirical analysis of human-bot interaction on reddit MC Ma, JP Lalor Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020 …, 2020	10	2020
Clustering examples in multi-dataset benchmarks with item response theory P Rodriguez, PM Htut, JP Lalor, J Sedoc Proceedings of the Third Workshop on Insights from Negative Results in NLP …, 2022	9	2022

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