| Counterfactual explanations for machine learning: A review S Verma, J Dickerson, K Hines arXiv preprint arXiv:2010.10596 2, 1, 2020 | 544 | 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 | 323 | 2019 |
| Counterfactual explanations and algorithmic recourses for machine learning: A review S Verma, V Boonsanong, M Hoang, K Hines, J Dickerson, C Shah ACM Computing Surveys 56 (12), 1-42, 2024 | 234 | 2024 |
| 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 | 189 | 2014 |
| A primer on Bayesian inference for biophysical systems KE Hines Biophysical journal 108 (9), 2103-2113, 2015 | 80 | 2015 |
| DeepTrax: Embedding Graphs of Financial Transactions CB Bruss, A Khazane, J Rider, R Serpe, A Gogoglou, KE Hines ICMLA, 2019 | 68* | 2019 |
| Benchmarking and defending against indirect prompt injection attacks on large language models J Yi, Y Xie, B Zhu, E Kiciman, G Sun, X Xie, F Wu arXiv preprint arXiv:2312.14197, 2023 | 66 | 2023 |
| Analyzing single-molecule time series via nonparametric Bayesian inference KE Hines, JR Bankston, RW Aldrich Biophysical journal 108 (3), 540-556, 2015 | 61 | 2015 |
| Inferring subunit stoichiometry from single molecule photobleaching KE Hines Biophysical Journal 104 (2), 527a, 2013 | 51 | 2013 |
| Counterfactual explanations for machine learning: Challenges revisited S Verma, J Dickerson, K Hines arXiv preprint arXiv:2106.07756, 2021 | 45 | 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 | 31 | 2022 |
| Defending Against Indirect Prompt Injection Attacks With Spotlighting K Hines, G Lopez, M Hall, F Zarfati, Y Zunger, E Kiciman arXiv preprint arXiv:2403.14720, 2024 | 27 | 2024 |
| 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 | 24 | 2022 |
| Counterfactual explanations for machine learning: A review. arXiv 2020 S Verma, J Dickerson, K Hines arXiv preprint arXiv:2010.10596, 0 | 22 | |
| On the interpretability and evaluation of graph representation learning A Gogoglou, CB Bruss, KE Hines arXiv preprint arXiv:1910.03081, 2019 | 14 | 2019 |
| Amortized generation of sequential counterfactual explanations for black-box models S Verma, K Hines, JP Dickerson CoRR, 2021 | 11 | 2021 |
| A Mutitask Network for Localization and Recognition of Text in Images R Sarshogh, KE Hines ICDAR, 2019 | 9 | 2019 |
| Reckoning with the disagreement problem: Explanation consensus as a training objective A Schwarzschild, M Cembalest, K Rao, K Hines, J Dickerson Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 662-678, 2023 | 7 | 2023 |
| Graph embeddings at scale CB Bruss, A Khazane, J Rider, R Serpe, S Nagrecha, KE Hines arXiv preprint arXiv:1907.01705, 2019 | 5 | 2019 |
| Repairing regressors for fair binary classification at any decision threshold K Kwegyir-Aggrey, AF Cooper, J Dai, J Dickerson, K Hines, ... arXiv preprint arXiv:2203.07490, 2022 | 4 | 2022 |
