Counterfactual explanations for machine learning: A review S Verma, J Dickerson, K Hines 2020 NeurIPS Workshop on ML Retrospectives, 2020 | 608* | 2020 |
Towards automated machine learning: Evaluation and comparison of AutoML approaches and tools A Truong, A Walters, J Goodsitt, K Hines, CB Bruss, R Farivar 2019 IEEE 31st international conference on tools with artificial …, 2019 | 263 | 2019 |
Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach KE Hines, TR Middendorf, RW Aldrich Journal of General Physiology 143 (3), 401-416, 2014 | 175 | 2014 |
A primer on Bayesian inference for biophysical systems KE Hines Biophysical journal 108 (9), 2103-2113, 2015 | 74 | 2015 |
Deeptrax: Embedding graphs of financial transactions A Khazane, J Rider, M Serpe, A Gogoglou, K Hines, CB Bruss, R Serpe 2019 18th IEEE International Conference On Machine Learning And Applications …, 2019 | 60 | 2019 |
Analyzing single-molecule time series via nonparametric Bayesian inference KE Hines, JR Bankston, RW Aldrich Biophysical journal 108 (3), 540-556, 2015 | 57 | 2015 |
Inferring subunit stoichiometry from single molecule photobleaching KE Hines Biophysical Journal 104 (2), 527a, 2013 | 48 | 2013 |
Counterfactual explanations for machine learning: Challenges revisited S Verma, J Dickerson, K Hines arXiv preprint arXiv:2106.07756, 2021 | 33 | 2021 |
Amortized generation of sequential algorithmic recourses for black-box models S Verma, K Hines, JP Dickerson Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8512-8519, 2022 | 24 | 2022 |
Benchmarking and defending against indirect prompt injection attacks on large language models J Yi, Y Xie, B Zhu, K Hines, E Kiciman, G Sun, X Xie, F Wu arXiv preprint arXiv:2312.14197, 2023 | 23 | 2023 |
Counterfactual explanations for machine learning: A review. arXiv 2020 S Verma, J Dickerson, K Hines arXiv preprint arXiv:2010.10596, 0 | 19 | |
Equalizing credit opportunity in algorithms: Aligning algorithmic fairness research with us fair lending regulation IE Kumar, KE Hines, JP Dickerson Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 357-368, 2022 | 18 | 2022 |
Neural embeddings of transaction data C Bruss, K Hines US Patent 10,789,530, 2020 | 14 | 2020 |
Systems and methods for text localization and recognition in an image of a document MR Sarshogh, K Hines US Patent 10,671,878, 2020 | 11 | 2020 |
On the interpretability and evaluation of graph representation learning A Gogoglou, CB Bruss, KE Hines 2019 NeurIPS Workshop on Graph Representation Learning, 2019 | 11 | 2019 |
Counterfactual explanations for machine learning: A review (2020). doi: 10.48550 S Verma, J Dickerson, K Hines arxiv, 2010 | 11 | 2010 |
Amortized Generation of Sequential Counterfactual Explanations for Black-box Models S Verma, K Hines, JP Dickerson AAAI 2022, 2021 | 10 | 2021 |
A Multitask Network for Localization and Recognition of Text in Images MR Sarshogh, KE Hines 2019 IEEE International Conference on Document Analysis and Recognition, 2019 | 8 | 2019 |
Machine learning monitoring systems and methods A Wenchel, J Dickerson, P Alexander, E O'sullivan, K Hines US Patent 11,922,280, 2024 | 6 | 2024 |
Systems and methods for targeting bid and position for a keyword JOH Montgomery, A Kintsakis, K Hines US Patent 10,937,058, 2021 | 6 | 2021 |