| 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 | 298 | 2019 |
| Saint: Improved neural networks for tabular data via row attention and contrastive pre-training G Somepalli, M Goldblum, A Schwarzschild, CB Bruss, T Goldstein arXiv preprint arXiv:2106.01342, 2021 | 219 | 2021 |
| 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 | 68 | 2019 |
| Culture and getting to yes: The linguistic signature of creative agreements in the United States and Egypt MJ Gelfand, L Severance, T Lee, CB Bruss, J Lun, AH Abdel‐Latif, ... Journal of Organizational Behavior 36 (7), 967-989, 2015 | 68 | 2015 |
| The cultural contagion of conflict M Gelfand, G Shteynberg, T Lee, J Lun, S Lyons, C Bell, JY Chiao, ... Philosophical Transactions of the Royal Society B: Biological Sciences 367 …, 2012 | 59 | 2012 |
| GOAT: A global transformer on large-scale graphs K Kong, J Chen, J Kirchenbauer, R Ni, CB Bruss, T Goldstein International Conference on Machine Learning, 17375-17390, 2023 | 41 | 2023 |
| Systems and methods of detecting email-based attacks through machine learning CB Bruss, S Fletcher, L Yu, J Kressel US Patent 10,397,272, 2019 | 41 | 2019 |
| Route planning system and methodology which account for safety factors PJ Ehsani, CB Bruss, JK Khodadad US Patent App. 14/205,495, 2015 | 40 | 2015 |
| Transfer learning with deep tabular models R Levin, V Cherepanova, A Schwarzschild, A Bansal, CB Bruss, ... arXiv preprint arXiv:2206.15306, 2022 | 37 | 2022 |
| Critical assessment of the foundations of power transmission and distribution reliability metrics and standards R Nateghi, SD Guikema, Y Wu, CB Bruss Risk analysis 36 (1), 4-15, 2016 | 31 | 2016 |
| Systems and methods of detecting email-based attacks through machine learning CB Bruss, S Fletcher, L Yu, J Kressel US Patent 10,805,347, 2020 | 29 | 2020 |
| Latent-cf: a simple baseline for reverse counterfactual explanations R Balasubramanian, S Sharpe, B Barr, J Wittenbach, CB Bruss arXiv preprint arXiv:2012.09301, 2020 | 26 | 2020 |
| Based-xai: Breaking ablation studies down for explainable artificial intelligence I Hameed, S Sharpe, D Barcklow, J Au-Yeung, S Verma, J Huang, B Barr, ... arXiv preprint arXiv:2207.05566, 2022 | 20 | 2022 |
| Neural embeddings of transaction data C Bruss, K Hines US Patent 10,789,530, 2020 | 15 | 2020 |
| On the interpretability and evaluation of graph representation learning A Gogoglou, CB Bruss, KE Hines arXiv preprint arXiv:1910.03081, 2019 | 13 | 2019 |
| A performance-driven benchmark for feature selection in tabular deep learning V Cherepanova, R Levin, G Somepalli, J Geiping, CB Bruss, AG Wilson, ... Advances in Neural Information Processing Systems 36, 41956-41979, 2023 | 12 | 2023 |
| Counterfactual explanations via latent space projection and interpolation B Barr, MR Harrington, S Sharpe, CB Bruss arXiv preprint arXiv:2112.00890, 2021 | 12 | 2021 |
| Saint: Improved neural networks for tabular data via row attention and contrastive pre-training. arXiv G Somepalli, M Goldblum, A Schwarzschild, CB Bruss, T Goldstein arXiv preprint arXiv:2106.01342, 2021 | 12 | 2021 |
| Towards ground truth explainability on tabular data B Barr, K Xu, C Silva, E Bertini, R Reilly, CB Bruss, JD Wittenbach arXiv preprint arXiv:2007.10532, 2020 | 12 | 2020 |
| Identifying interpretable subspaces in image representations N Kalibhat, S Bhardwaj, CB Bruss, H Firooz, M Sanjabi, S Feizi International Conference on Machine Learning, 15623-15638, 2023 | 11 | 2023 |
