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 | 83 | 2016 |
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 | 61 | 2022 |
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 | 58 | 2021 |
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 | 45 | 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 | 44* | 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 | 33 | 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 | 32 | 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 | 24 | 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 | 24 | 2018 |
Dynamic data selection for curriculum learning via ability estimation JP Lalor, H Yu Proceedings of the Conference on Empirical Methods in Natural Language …, 2020 | 22 | 2020 |
CIFT: Crowd-informed fine-tuning to improve machine learning ability JP Lalor, H Wu, H Yu arXiv preprint arXiv:1702.08563, 2017 | 21* | 2017 |
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 | 20 | 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 | 19 | 2019 |
Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls C Miller, A Settle, J Lalor | 17 | 2015 |
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 | 15 | 2021 |
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 | 9 | 2020 |
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 | 8 | 2024 |
py-irt: A Scalable Item Response Theory Library for Python JP Lalor, P Rodriguez INFORMS Journal on Computing 35 (1), 5-13, 2023 | 8 | 2023 |
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 | 6 | 2022 |