| Counterfactual explanations for machine learning: A review S Verma, J Dickerson, K Hines 2020 NeurIPS Workshop on ML Retrospectives, 2020 | 343* | 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 | 178 | 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 | 147 | 2014 |
| A primer on Bayesian inference for biophysical systems KE Hines Biophysical journal 108 (9), 2103-2113, 2015 | 66 | 2015 |
| Inferring subunit stoichiometry from single molecule photobleaching KE Hines Journal of General Physiology 141 (6), 737-746, 2013 | 48 | 2013 |
| Analyzing single-molecule time series via nonparametric Bayesian inference KE Hines, JR Bankston, RW Aldrich Biophysical journal 108 (3), 540-556, 2015 | 47 | 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 | 40 | 2019 |
| Counterfactual explanations for machine learning: A review. arXiv 2020 S Verma, J Dickerson, K Hines arXiv preprint arXiv:2010.10596, 0 | 16 | |
| Counterfactual explanations for machine learning: Challenges revisited S Verma, J Dickerson, K Hines arXiv preprint arXiv:2106.07756, 2021 | 15 | 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 | 7 | 2022 |
| Systems and methods for text localization and recognition in an image of a document MR Sarshogh, K Hines US Patent 10,671,878, 2020 | 7 | 2020 |
| 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 | 7 | 2019 |
| Neural embeddings of transaction data C Bruss, K Hines US Patent 10,789,530, 2020 | 6 | 2020 |
| On the interpretability and evaluation of graph representation learning A Gogoglou, CB Bruss, KE Hines 2019 NeurIPS Workshop on Graph Representation Learning, 2019 | 5 | 2019 |
| 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 | 4 | 2022 |
| Amortized Generation of Sequential Counterfactual Explanations for Black-box Models S Verma, K Hines, JP Dickerson AAAI 2022, 2021 | 4 | 2021 |
| Graph embeddings at scale CB Bruss, A Khazane, J Rider, R Serpe, S Nagrecha, KE Hines arXiv preprint arXiv:1907.01705, 2019 | 4 | 2019 |
| Counterfactual explanations for machine learning: A review (2020). doi: 10.48550 S Verma, J Dickerson, K Hines ARXIV, 2010 | 4 | 2010 |
| Anomaly Detection in Cyber Network Data Using a Cyber Language Approach BD Richardson, BJ Radford, SE Davis, K Hines, D Pekarek arXiv preprint arXiv:1808.10742, 2018 | 3 | 2018 |
| Credit decisioning based on graph neural networks MR Sarshogh, C Bruss, K Hines US Patent App. 17/556,397, 2022 | 2 | 2022 |
