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ORCIDJohn Lalor
Person information
- affiliation: University of Notre Dame, IN, USA
- affiliation (PhD 2019): University of Massachusetts, Amherst, MA, USA
- affiliation (former): DePaul University, Chicago, IL, USA
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2020 – today
- 2025
[j5]Yi Yang
, John P. Lalor, Ahmed Abbasi, Daniel Dajun Zeng:
Hierarchical Deep Document Model. IEEE Trans. Knowl. Data Eng. 37(1): 351-364 (2025)- [i14]Kezia Oketch, John P. Lalor, Yi Yang, Ahmed Abbasi:
Bridging the LLM Accessibility Divide? Performance, Fairness, and Cost of Closed versus Open LLMs for Automated Essay Scoring. CoRR abs/2503.11827 (2025) - 2024
- [j4]John P. Lalor, Ahmed Abbasi, Kezia Oketch, Yi Yang, Nicole Forsgren:
Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines. ACM Trans. Inf. Syst. 42(4): 99:1-99:41 (2024) - [i13]Guangyu Meng, Qingkai Zeng, John P. Lalor, Hong Yu:
A Psychology-based Unified Dynamic Framework for Curriculum Learning. CoRR abs/2408.05326 (2024) - [i12]Jung Hoon Lim, Sunjae Kwon, Zonghai Yao, John P. Lalor, Hong Yu:
Large Language Model-based Role-Playing for Personalized Medical Jargon Extraction. CoRR abs/2408.05555 (2024) - 2023
- [j3]John P. Lalor, Hao Wu, Kathleen M. Mazor, Hong Yu:
Evaluating the efficacy of NoteAid on EHR note comprehension among US Veterans through Amazon Mechanical Turk. Int. J. Medical Informatics 172: 105006 (2023) - [j2]John Patrick Lalor, Pedro Rodríguez:
py-irt: A Scalable Item Response Theory Library for Python. INFORMS J. Comput. 35(1): 5-13 (2023) - [j1]Kaitlin D. Wowak, John P. Lalor, Sriram Somanchi, Corey M. Angst:
Business Analytics in Healthcare: Past, Present, and Future Trends. Manuf. Serv. Oper. Manag. 25(3): 975-995 (2023) - [i11]Wenchang Li, Yixing Chen, John P. Lalor:
Stars Are All You Need: A Distantly Supervised Pyramid Network for Document-Level End-to-End Sentiment Analysis. CoRR abs/2305.01710 (2023) - [i10]Yi Yang, Hanyu Duan, Ahmed Abbasi, John P. Lalor, Kar Yan Tam:
Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads. CoRR abs/2311.10395 (2023) - [i9]Xiaojing Duan, John P. Lalor:
H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognition. CoRR abs/2311.11981 (2023) - 2022
- [c19]Pedro Rodríguez, Phu Mon Htut, John Lalor, João Sedoc:
Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory. Insights@ACL 2022: 100-112 - [c18]John Lalor, Yi Yang, Kendall Smith, Nicole Forsgren, Ahmed Abbasi:
Benchmarking Intersectional Biases in NLP. NAACL-HLT 2022: 3598-3609 - [i8]John P. Lalor, Pedro Rodríguez:
py-irt: A Scalable Item Response Theory Library for Python. CoRR abs/2203.01282 (2022) - [i7]John P. Lalor, Hong Guo:
Measuring algorithmic interpretability: A human-learning-based framework and the corresponding cognitive complexity score. CoRR abs/2205.10207 (2022) - 2021
- [c17]Pedro Rodriguez, Joe Barrow, Alexander Miserlis Hoyle, John P. Lalor, Robin Jia, Jordan L. Boyd-Graber:
Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? ACL/IJCNLP (1) 2021: 4486-4503 - [c16]Ahmed Abbasi, David G. Dobolyi, John P. Lalor, Richard G. Netemeyer, Kendall Smith, Yi Yang:
Constructing a Psychometric Testbed for Fair Natural Language Processing. EMNLP (1) 2021: 3748-3758 - [c15]Nicholas Berente, John P. Lalor, Sriram Somanchi, Ahmed Abbasi:
The Illusion of Certainty and Data-Driven Decision Making in Emergent Situations. ICIS 2021 - [c14]Hani Safadi, John P. Lalor, Nicholas Berente:
The Effect of Bots on Human Interaction in Online Communities. ICIS 2021 - 2020
- [c13]Ming-Cheng Ma, John P. Lalor:
An Empirical Analysis of Human-Bot Interaction on Reddit. W-NUT@EMNLP 2020: 101-106 - [c12]John P. Lalor, Hong Yu:
Dynamic Data Selection for Curriculum Learning via Ability Estimation. EMNLP (Findings) 2020: 545-555 - [i6]John P. Lalor, Hong Yu:
Dynamic Data Selection for Curriculum Learning via Ability Estimation. CoRR abs/2011.00080 (2020)
2010 – 2019
- 2019
- [c11]Eunah Cho, He Xie, John P. Lalor, Varun Kumar, William M. Campbell:
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity. ASRU 2019: 1077-1084 - [c10]John P. Lalor, Hao Wu, Hong Yu:
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds. EMNLP/IJCNLP (1) 2019: 4248-4258 - [i5]John P. Lalor, Hao Wu, Hong Yu:
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds. CoRR abs/1908.11421 (2019) - [i4]Eunah Cho, He Xie, John P. Lalor, Varun Kumar, William M. Campbell:
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity. CoRR abs/1910.04196 (2019) - 2018
- [c9]Jinying Chen, John P. Lalor, Hong Yu:
Detecting Hypoglycemia Incidents from Patients' Secure Messages. AMIA 2018 - [c8]John Lalor, Hao Wu, Tsendsuren Munkhdalai, Hong Yu:
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study. EMNLP 2018: 4711-4716 - 2017
- [c7]John P. Lalor, Hao Wu, Li Chen, Kathleen M. Mazor, Hong Yu:
Generating a Test of Electronic Health Record Narrative Comprehension with Item Response Theory. AMIA 2017 - [i3]John P. Lalor, Hao Wu, Tsendsuren Munkhdalai, Hong Yu:
An Analysis of Machine Learning Intelligence. CoRR abs/1702.04811 (2017) - [i2]John P. Lalor, Hao Wu, Hong Yu:
Improving Machine Learning Ability with Fine-Tuning. CoRR abs/1702.08563 (2017) - 2016
- [c6]Tsendsuren Munkhdalai, John Lalor, Hong Yu:
Citation Analysis with Neural Attention Models. Louhi@EMNLP 2016: 69-77 - [c5]John P. Lalor, Hao Wu, Hong Yu:
Building an Evaluation Scale using Item Response Theory. EMNLP 2016: 648-657 - [i1]John P. Lalor, Hao Wu, Hong Yu:
Beyond Majority Voting: Generating Evaluation Scales using Item Response Theory. CoRR abs/1605.08889 (2016) - 2015
- [c4]Amber Settle, John Lalor, Theresa A. Steinbach:
A Computer Science Linked-courses Learning Community. ITiCSE 2015: 123-128 - [c3]Amber Settle, John Lalor, Theresa A. Steinbach:
Reconsidering the Impact of CS1 on Novice Attitudes. SIGCSE 2015: 229-234 - [c2]Craig S. Miller, Amber Settle, John Lalor:
Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls. SIGITE 2015: 59-64 - [c1]Amber Settle, John Lalor, Theresa A. Steinbach:
Evaluating a Linked-courses Learning Community for Development Majors. SIGITE 2015: 127-132
Coauthor Index
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last updated on 2025-04-14 21:11 CEST by the dblp team
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