| An empirical evaluation of generic convolutional and recurrent networks for sequence modeling S Bai, JZ Kolter, V Koltun arXiv preprint arXiv:1803.01271, 2018 | 5203 | 2018 |
| Certified adversarial robustness via randomized smoothing J Cohen, E Rosenfeld, Z Kolter international conference on machine learning, 1310-1320, 2019 | 1865 | 2019 |
| REDD: A public data set for energy disaggregation research JZ Kolter, MJ Johnson Workshop on data mining applications in sustainability (SIGKDD), San Diego …, 2011 | 1772 | 2011 |
| Towards fully autonomous driving: Systems and algorithms J Levinson, J Askeland, J Becker, J Dolson, D Held, S Kammel, JZ Kolter, ... 2011 IEEE intelligent vehicles symposium (IV), 163-168, 2011 | 1678 | 2011 |
| Provable defenses against adversarial examples via the convex outer adversarial polytope E Wong, Z Kolter International conference on machine learning, 5286-5295, 2018 | 1584 | 2018 |
| Dynamic weighted majority: An ensemble method for drifting concepts JZ Kolter, MA Maloof The Journal of Machine Learning Research 8, 2755-2790, 2007 | 1329 | 2007 |
| Fast is better than free: Revisiting adversarial training E Wong, L Rice, JZ Kolter arXiv preprint arXiv:2001.03994, 2020 | 1143 | 2020 |
| Multimodal transformer for unaligned multimodal language sequences YHH Tsai, S Bai, PP Liang, JZ Kolter, LP Morency, R Salakhutdinov Proceedings of the conference. Association for computational linguistics …, 2019 | 1126 | 2019 |
| Optnet: Differentiable optimization as a layer in neural networks B Amos, JZ Kolter International Conference on Machine Learning, 136-145, 2017 | 927 | 2017 |
| Learning to detect and classify malicious executables in the wild. JZ Kolter, MA Maloof Journal of Machine Learning Research 7 (12), 2006 | 890 | 2006 |
| Approximate inference in additive factorial hmms with application to energy disaggregation JZ Kolter, T Jaakkola Artificial intelligence and statistics, 1472-1482, 2012 | 833 | 2012 |
| Overfitting in adversarially robust deep learning L Rice, E Wong, Z Kolter International conference on machine learning, 8093-8104, 2020 | 795 | 2020 |
| Learning to detect malicious executables in the wild JZ Kolter, MA Maloof Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 717 | 2004 |
| Deep equilibrium models S Bai, JZ Kolter, V Koltun Advances in neural information processing systems 32, 2019 | 605 | 2019 |
| Differentiable convex optimization layers A Agrawal, B Amos, S Barratt, S Boyd, S Diamond, JZ Kolter Advances in neural information processing systems 32, 2019 | 580 | 2019 |
| Input convex neural networks B Amos, L Xu, JZ Kolter International Conference on Machine Learning, 146-155, 2017 | 571 | 2017 |
| Energy disaggregation via discriminative sparse coding J Kolter, S Batra, A Ng Advances in neural information processing systems 23, 2010 | 488 | 2010 |
| Scaling provable adversarial defenses E Wong, F Schmidt, JH Metzen, JZ Kolter Advances in Neural Information Processing Systems 31, 2018 | 449 | 2018 |
| End-to-end differentiable physics for learning and control F de Avila Belbute-Peres, K Smith, K Allen, J Tenenbaum, JZ Kolter Advances in neural information processing systems 31, 2018 | 405 | 2018 |
| An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv 2018 S Bai, JZ Kolter, V Koltun arXiv preprint arXiv:1803.01271 2 (1803), 8, 1803 | 396 | 1803 |
